import pandas as pd
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import seaborn as sns
import mlxtend
import lifetimes
df = pd.read_csv(r'C:\Users\HP\Desktop\market\segm\sales_data.csv')
df
| Order Date | Order ID | Product | Product_ean | catégorie | Purchase Address | Quantity Ordered | Price Each | Cost price | turnover | margin | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-01-22 21:25:00 | 141234 | iPhone | 5.638009e+12 | Vêtements | 944 Walnut St, Boston, MA 02215 | 1 | 700.00 | 231.0000 | 700.00 | 469.0000 |
| 1 | 2019-01-28 14:15:00 | 141235 | Lightning Charging Cable | 5.563320e+12 | Alimentation | 185 Maple St, Portland, OR 97035 | 1 | 14.95 | 7.4750 | 14.95 | 7.4750 |
| 2 | 2019-01-17 13:33:00 | 141236 | Wired Headphones | 2.113973e+12 | Vêtements | 538 Adams St, San Francisco, CA 94016 | 2 | 11.99 | 5.9950 | 23.98 | 11.9900 |
| 3 | 2019-01-05 20:33:00 | 141237 | 27in FHD Monitor | 3.069157e+12 | Sports | 738 10th St, Los Angeles, CA 90001 | 1 | 149.99 | 97.4935 | 149.99 | 52.4965 |
| 4 | 2019-01-25 11:59:00 | 141238 | Wired Headphones | 9.692681e+12 | Électronique | 387 10th St, Austin, TX 73301 | 1 | 11.99 | 5.9950 | 11.99 | 5.9950 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 185945 | 2019-12-11 20:58:00 | 319666 | Lightning Charging Cable | 6.545974e+12 | Électronique | 14 Madison St, San Francisco, CA 94016 | 1 | 14.95 | 7.4750 | 14.95 | 7.4750 |
| 185946 | 2019-12-01 12:01:00 | 319667 | AA Batteries (4-pack) | 5.352480e+12 | Électronique | 549 Willow St, Los Angeles, CA 90001 | 2 | 3.84 | 1.9200 | 7.68 | 3.8400 |
| 185947 | 2019-12-09 06:43:00 | 319668 | Vareebadd Phone | 2.674213e+12 | Alimentation | 273 Wilson St, Seattle, WA 98101 | 1 | 400.00 | 132.0000 | 400.00 | 268.0000 |
| 185948 | 2019-12-03 10:39:00 | 319669 | Wired Headphones | 5.216304e+12 | Alimentation | 778 River St, Dallas, TX 75001 | 1 | 11.99 | 5.9950 | 11.99 | 5.9950 |
| 185949 | 2019-12-21 21:45:00 | 319670 | Bose SoundSport Headphones | 8.081038e+12 | Électronique | 747 Chestnut St, Los Angeles, CA 90001 | 1 | 99.99 | 49.9950 | 99.99 | 49.9950 |
185950 rows × 11 columns
df.dtypes
Order Date object Order ID int64 Product object Product_ean float64 catégorie object Purchase Address object Quantity Ordered int64 Price Each float64 Cost price float64 turnover float64 margin float64 dtype: object
df['Order Date'] = pd.to_datetime(df['Order Date'])
df['month'] = df['Order Date'].dt.month
df['year'] = df['Order Date'].dt.year
df['Order Date1']=df['Order Date'].dt.to_period('M').dt.to_timestamp('M')
df['state']=df['Purchase Address'].apply(lambda x : x.split(',')[1])
le = LabelEncoder()
df['customer_id']=le.fit_transform(df[['Purchase Address']])
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\preprocessing\_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True)
df.drop(columns = 'Purchase Address',inplace=True)
df
| Order Date | Order ID | Product | Product_ean | catégorie | Quantity Ordered | Price Each | Cost price | turnover | margin | month | year | Order Date1 | state | customer_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-01-22 21:25:00 | 141234 | iPhone | 5.638009e+12 | Vêtements | 1 | 700.00 | 231.0000 | 700.00 | 469.0000 | 1 | 2019 | 2019-01-31 | Boston | 132266 |
| 1 | 2019-01-28 14:15:00 | 141235 | Lightning Charging Cable | 5.563320e+12 | Alimentation | 1 | 14.95 | 7.4750 | 14.95 | 7.4750 | 1 | 2019 | 2019-01-31 | Portland | 13519 |
| 2 | 2019-01-17 13:33:00 | 141236 | Wired Headphones | 2.113973e+12 | Vêtements | 2 | 11.99 | 5.9950 | 23.98 | 11.9900 | 1 | 2019 | 2019-01-31 | San Francisco | 68669 |
| 3 | 2019-01-05 20:33:00 | 141237 | 27in FHD Monitor | 3.069157e+12 | Sports | 1 | 149.99 | 97.4935 | 149.99 | 52.4965 | 1 | 2019 | 2019-01-31 | Los Angeles | 99824 |
| 4 | 2019-01-25 11:59:00 | 141238 | Wired Headphones | 9.692681e+12 | Électronique | 1 | 11.99 | 5.9950 | 11.99 | 5.9950 | 1 | 2019 | 2019-01-31 | Austin | 44894 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 185945 | 2019-12-11 20:58:00 | 319666 | Lightning Charging Cable | 6.545974e+12 | Électronique | 1 | 14.95 | 7.4750 | 14.95 | 7.4750 | 12 | 2019 | 2019-12-31 | San Francisco | 6487 |
| 185946 | 2019-12-01 12:01:00 | 319667 | AA Batteries (4-pack) | 5.352480e+12 | Électronique | 2 | 3.84 | 1.9200 | 7.68 | 3.8400 | 12 | 2019 | 2019-12-31 | Los Angeles | 70444 |
| 185947 | 2019-12-09 06:43:00 | 319668 | Vareebadd Phone | 2.674213e+12 | Alimentation | 1 | 400.00 | 132.0000 | 400.00 | 268.0000 | 12 | 2019 | 2019-12-31 | Seattle | 27410 |
| 185948 | 2019-12-03 10:39:00 | 319669 | Wired Headphones | 5.216304e+12 | Alimentation | 1 | 11.99 | 5.9950 | 11.99 | 5.9950 | 12 | 2019 | 2019-12-31 | Dallas | 106162 |
| 185949 | 2019-12-21 21:45:00 | 319670 | Bose SoundSport Headphones | 8.081038e+12 | Électronique | 1 | 99.99 | 49.9950 | 99.99 | 49.9950 | 12 | 2019 | 2019-12-31 | Los Angeles | 101282 |
185950 rows × 15 columns
df['Min_date']=df.groupby("customer_id")["Order Date"].transform('min')
df['Min_date']=df['Min_date'].dt.to_period('M').dt.to_timestamp('M')
df['diff']=(df['Order Date1']-df['Min_date'])
df['diff']=(df['diff'].dt.days/30).astype(int)+1
cohort_table = df.pivot_table(index = 'Min_date' ,columns = 'diff' ,values='Order ID' ,aggfunc='count')
cohort_table = cohort_table.div(cohort_table[1],axis=0)
plt.subplots(figsize=(14,8))
sns.heatmap(cohort_table ,cmap='Blues' ,annot=True,fmt='.5g')
plt.show()
cohort_table1 = df.pivot_table(index = 'Min_date' ,columns = 'diff' ,values='Price Each' ,aggfunc='sum')
cohort_table1 = cohort_table1.div(cohort_table1[1],axis=0)
plt.subplots(figsize=(18,8))
sns.heatmap(cohort_table1 ,cmap='Blues' ,annot=True,fmt='.8g')
plt.show()
df['state']=df['state'].str.strip()
df['state'].unique()
array(['Boston', 'Portland', 'San Francisco', 'Los Angeles', 'Austin',
'Atlanta', 'Seattle', 'New York City', 'Dallas'], dtype=object)
for _ in df['state'].unique():
cohort_table = df.query(f"state == '{_}'").pivot_table(index = 'Min_date' ,columns = 'diff' ,values='Order ID' ,aggfunc='count')
cohort_table = cohort_table.div(cohort_table[1],axis=0)
plt.subplots(figsize=(14,8))
sns.heatmap(cohort_table ,cmap='Blues' ,annot=True,fmt='.5g')
plt.show()
from lifetimes.utils import summary_data_from_transaction_data
from lifetimes import GammaGammaFitter,ModifiedBetaGeoFitter,BetaGeoBetaBinomFitter
T = summary_data_from_transaction_data(transactions=df ,customer_id_col='customer_id' ,datetime_col='Order Date' ,monetary_value_col='Price Each')
T = T.query("monetary_value > 0")
gamma = GammaGammaFitter(penalizer_coef=0.00001)
T.corr()
| frequency | recency | T | monetary_value | |
|---|---|---|---|---|
| frequency | 1.000000 | 0.301791 | 0.155754 | 0.011661 |
| recency | 0.301791 | 1.000000 | 0.565643 | 0.009438 |
| T | 0.155754 | 0.565643 | 1.000000 | 0.009844 |
| monetary_value | 0.011661 | 0.009438 | 0.009844 | 1.000000 |
gamma.fit(T['frequency'] ,T['monetary_value'])
<lifetimes.GammaGammaFitter: fitted with 30724 subjects, p: 2.49, q: 0.44, v: 2.04>
mbg = ModifiedBetaGeoFitter(penalizer_coef=0.00001)
mbg.fit(T['frequency'],T['recency'],T['T'])
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\arraylike.py:397: RuntimeWarning: invalid value encountered in sqrt result = getattr(ufunc, method)(*inputs, **kwargs)
<lifetimes.ModifiedBetaGeoFitter: fitted with 30724 subjects, a: 0.00, alpha: 4839.27, b: 0.00, r: 25.24>
from lifetimes.plotting import plot_probability_alive_matrix
from lifetimes.plotting import plot_frequency_recency_matrix
from lifetimes.plotting import plot_expected_repeat_purchases
plt.subplots(figsize=(14,8))
plot_probability_alive_matrix(model=mbg)
plt.show()
plt.subplots( figsize=(14, 8))
plot_frequency_recency_matrix(model=mbg, T=30 )
plt.show()
plt.subplots(figsize=(14, 8))
plot_expected_repeat_purchases(model=mbg)
plt.show()
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
gamma.fit(frequency=T['frequency'] ,monetary_value=T['monetary_value'])
<lifetimes.GammaGammaFitter: fitted with 30724 subjects, p: 11.35, q: 0.50, v: 0.56>
cltv = gamma.customer_lifetime_value(transaction_prediction_model=mbg ,frequency=T['frequency'] ,recency=T['recency'] ,T = T['T'] ,monetary_value = T['monetary_value'] ,time=1 ,discount_rate=0)
cltv
customer_id
5 285.824104
7 2.499445
18 1.990330
22 2.049728
25 0.710052
...
140750 17.972122
140757 0.565830
140768 96.132788
140769 2.072777
140781 2.007943
Name: clv, Length: 30724, dtype: float64
states = df.groupby("customer_id")["state"].unique().apply(lambda x : x[0])
states = states.to_frame()
cltv = cltv.to_frame()
final_cltv = states.merge(cltv ,how = 'left',right_index=True ,left_index=True)
final_cltv = final_cltv.fillna(0)
final_cltv['clv']=final_cltv['clv']/final_cltv['clv'].max()
def groups(x):
if x <= 0.00508:
return 'weak'
elif x > 0.00508 and x <= 0.0601:
return 'medium'
else :
return 'strong'
final_cltv['clv_gro'] = final_cltv['clv'].apply(groups)
final_cltv.query("clv > 0")
| state | clv | clv_gro | |
|---|---|---|---|
| customer_id | |||
| 5 | San Francisco | 0.708835 | strong |
| 7 | New York City | 0.006199 | medium |
| 18 | Los Angeles | 0.004936 | weak |
| 22 | San Francisco | 0.005083 | medium |
| 25 | Los Angeles | 0.001761 | weak |
| ... | ... | ... | ... |
| 140750 | Los Angeles | 0.044570 | medium |
| 140757 | Los Angeles | 0.001403 | weak |
| 140768 | Los Angeles | 0.238406 | strong |
| 140769 | San Francisco | 0.005140 | medium |
| 140781 | Seattle | 0.004980 | weak |
30724 rows × 3 columns
plt.subplots(figsize=(14,8))
sns.countplot(data=final_cltv ,x= 'clv_gro')
sns.despine()
plt.show()
plt.subplots(figsize=(14,8))
sns.countplot(data=final_cltv ,x='state' ,hue = 'clv_gro')
sns.despine()
plt.show()
import numpy as np
plt.subplots(figsize=(14,8))
sns.barplot(data = df ,x='state' ,y='Price Each' ,estimator=np.sum ,order=['Austin', 'Portland', 'Seattle', 'Dallas', 'Atlanta', 'Boston',
'New York City', 'Los Angeles', 'San Francisco'])
sns.lineplot(data=df.groupby(["state"])['Quantity Ordered'].sum().sort_values().reset_index() ,x='state' ,y='Quantity Ordered' ,marker='s', color='red')
sns.despine()
plt.yscale('log')
plt.show()
x = df.pivot_table(index = 'state' ,columns = 'Order Date1' ,values = 'Price Each' ,aggfunc='sum' ,margins=True)
x
| Order Date1 | 2019-01-31 00:00:00 | 2019-02-28 00:00:00 | 2019-03-31 00:00:00 | 2019-04-30 00:00:00 | 2019-05-31 00:00:00 | 2019-06-30 00:00:00 | 2019-07-31 00:00:00 | 2019-08-31 00:00:00 | 2019-09-30 00:00:00 | 2019-10-31 00:00:00 | 2019-11-30 00:00:00 | 2019-12-31 00:00:00 | 2020-01-31 00:00:00 | All |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| state | ||||||||||||||
| Atlanta | 148523.69 | 175851.81 | 230134.86 | 282870.88 | 237572.66 | 218858.60 | 210979.19 | 168443.29 | 169923.85 | 304508.46 | 273143.85 | 359097.06 | NaN | 2779908.20 |
| Austin | 87163.21 | 108325.58 | 153671.83 | 171487.65 | 159802.17 | 143193.06 | 149775.88 | 125146.00 | 105825.92 | 202100.25 | 170441.96 | 232440.12 | 499.98 | 1809873.61 |
| Boston | 198912.69 | 213629.24 | 298970.37 | 351669.70 | 325999.80 | 252741.72 | 290151.72 | 236932.64 | 246591.73 | 364265.09 | 349623.53 | 506594.65 | 1326.89 | 3637409.77 |
| Dallas | 141158.53 | 185438.29 | 221205.40 | 250209.05 | 267131.07 | 185841.21 | 211201.99 | 178958.29 | 163076.03 | 321353.23 | 247127.45 | 378212.33 | 1714.95 | 2752627.82 |
| Los Angeles | 285754.49 | 339637.47 | 427827.95 | 546855.97 | 497151.19 | 449351.78 | 392183.65 | 344302.64 | 352525.82 | 609228.15 | 496374.57 | 679241.56 | 999.99 | 5421435.23 |
| New York City | 256608.67 | 303046.15 | 365148.88 | 445252.65 | 433439.97 | 322462.56 | 353338.28 | 301152.88 | 298981.09 | 485336.03 | 425707.06 | 643612.89 | 1283.72 | 4635370.83 |
| Portland | 114310.51 | 148571.15 | 185649.95 | 238942.93 | 230922.52 | 168857.59 | 175397.95 | 151878.13 | 131816.96 | 252024.64 | 206659.16 | 302562.99 | 152.99 | 2307747.47 |
| San Francisco | 431292.63 | 544218.19 | 690363.85 | 805693.57 | 772384.31 | 607694.75 | 638095.00 | 535065.50 | 460938.06 | 861937.47 | 760902.41 | 1100501.11 | 2374.89 | 8211461.74 |
| Seattle | 139540.56 | 170166.84 | 218234.74 | 274688.62 | 210721.44 | 213024.34 | 211415.90 | 188466.05 | 155312.63 | 314801.51 | 250620.69 | 386152.70 | 149.99 | 2733296.01 |
| All | 1803264.98 | 2188884.72 | 2791207.83 | 3367671.02 | 3135125.13 | 2562025.61 | 2632539.56 | 2230345.42 | 2084992.09 | 3715554.83 | 3180600.68 | 4588415.41 | 8503.40 | 34289130.68 |
x = x.div(x.iloc[-1,:])*100
x = round(x,2)
plt.subplots(figsize=(18,8))
sns.heatmap(x ,annot=True ,cmap='Blues',fmt='.8g')
plt.show()
df
| Order Date | Order ID | Product | Product_ean | catégorie | Quantity Ordered | Price Each | Cost price | turnover | margin | month | year | Order Date1 | state | customer_id | Min_date | diff | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-01-22 21:25:00 | 141234 | iPhone | 5.638009e+12 | Vêtements | 1 | 700.00 | 231.0000 | 700.00 | 469.0000 | 1 | 2019 | 2019-01-31 | Boston | 132266 | 2019-01-31 | 1 |
| 1 | 2019-01-28 14:15:00 | 141235 | Lightning Charging Cable | 5.563320e+12 | Alimentation | 1 | 14.95 | 7.4750 | 14.95 | 7.4750 | 1 | 2019 | 2019-01-31 | Portland | 13519 | 2019-01-31 | 1 |
| 2 | 2019-01-17 13:33:00 | 141236 | Wired Headphones | 2.113973e+12 | Vêtements | 2 | 11.99 | 5.9950 | 23.98 | 11.9900 | 1 | 2019 | 2019-01-31 | San Francisco | 68669 | 2019-01-31 | 1 |
| 3 | 2019-01-05 20:33:00 | 141237 | 27in FHD Monitor | 3.069157e+12 | Sports | 1 | 149.99 | 97.4935 | 149.99 | 52.4965 | 1 | 2019 | 2019-01-31 | Los Angeles | 99824 | 2019-01-31 | 1 |
| 4 | 2019-01-25 11:59:00 | 141238 | Wired Headphones | 9.692681e+12 | Électronique | 1 | 11.99 | 5.9950 | 11.99 | 5.9950 | 1 | 2019 | 2019-01-31 | Austin | 44894 | 2019-01-31 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 185945 | 2019-12-11 20:58:00 | 319666 | Lightning Charging Cable | 6.545974e+12 | Électronique | 1 | 14.95 | 7.4750 | 14.95 | 7.4750 | 12 | 2019 | 2019-12-31 | San Francisco | 6487 | 2019-12-31 | 1 |
| 185946 | 2019-12-01 12:01:00 | 319667 | AA Batteries (4-pack) | 5.352480e+12 | Électronique | 2 | 3.84 | 1.9200 | 7.68 | 3.8400 | 12 | 2019 | 2019-12-31 | Los Angeles | 70444 | 2019-05-31 | 8 |
| 185947 | 2019-12-09 06:43:00 | 319668 | Vareebadd Phone | 2.674213e+12 | Alimentation | 1 | 400.00 | 132.0000 | 400.00 | 268.0000 | 12 | 2019 | 2019-12-31 | Seattle | 27410 | 2019-12-31 | 1 |
| 185948 | 2019-12-03 10:39:00 | 319669 | Wired Headphones | 5.216304e+12 | Alimentation | 1 | 11.99 | 5.9950 | 11.99 | 5.9950 | 12 | 2019 | 2019-12-31 | Dallas | 106162 | 2019-12-31 | 1 |
| 185949 | 2019-12-21 21:45:00 | 319670 | Bose SoundSport Headphones | 8.081038e+12 | Électronique | 1 | 99.99 | 49.9950 | 99.99 | 49.9950 | 12 | 2019 | 2019-12-31 | Los Angeles | 101282 | 2019-12-31 | 1 |
185950 rows × 17 columns
abc = df.groupby("Product")["Price Each"].sum().sort_values(ascending=False).reset_index()
abc['Price Each'].sum()
34289130.68000001
abc['portion']=abc['Price Each'] / abc['Price Each'].sum()
abc['cumsum']=abc['portion'].cumsum()
def seg(x):
if x <= 0.8:
return 'x'
elif x >0.8 and x <=0.9:
return 'y'
else :
return 'z'
abc['group']=abc['cumsum'].apply(seg)
plt.subplots(figsize=(14,8))
sns.barplot(data=abc ,x='Product' ,y='portion' ,hue='group')
sns.despine()
plt.xticks(rotation=90)
plt.show()
abc_months = df.groupby(["Order Date1","Product"])["Price Each"].sum().reset_index()
abc_months['total']=abc_months.groupby("Order Date1")['Price Each'].transform('sum')
abc_months['portaion']=abc_months['Price Each'] /abc_months['total']
abc_months = abc_months.sort_values(by=['Order Date1','portaion'],ascending=[True ,False])
abc_months['cumsum']=abc_months.groupby("Order Date1")["portaion"].cumsum()
abc_months['group']=abc_months['cumsum'].apply(seg)
plt.subplots(figsize=(14,8))
sns.heatmap(abc_months[['Order Date1','Product','group','cumsum']].query("group == 'x'").pivot_table(index = ['Order Date1','group'],columns='Product' ,aggfunc='count'),cmap='Blues' ,annot=True)
plt.show()
target=df[df['Product'].isin(['27in 4K Gaming Monitor', '27in FHD Monitor', '34in Ultrawide Monitor',
'Apple Airpods Headphones', 'Google Phone', 'Macbook Pro Laptop',
'ThinkPad Laptop', 'iPhone'])]
target=target.groupby(["state","Product"])['Price Each'].sum().reset_index().pivot_table(index='state' ,columns='Product' ,values='Price Each' ,aggfunc='sum',margins=True)
target=target.div(target.iloc[-1])
target=round(target,2)
target
| Product | 27in 4K Gaming Monitor | 27in FHD Monitor | 34in Ultrawide Monitor | Apple Airpods Headphones | Google Phone | Macbook Pro Laptop | ThinkPad Laptop | iPhone | All |
|---|---|---|---|---|---|---|---|---|---|
| state | |||||||||
| Atlanta | 0.08 | 0.08 | 0.08 | 0.08 | 0.08 | 0.08 | 0.09 | 0.08 | 0.08 |
| Austin | 0.05 | 0.05 | 0.05 | 0.06 | 0.05 | 0.05 | 0.05 | 0.06 | 0.05 |
| Boston | 0.11 | 0.11 | 0.11 | 0.11 | 0.11 | 0.10 | 0.11 | 0.11 | 0.11 |
| Dallas | 0.08 | 0.08 | 0.08 | 0.08 | 0.08 | 0.08 | 0.08 | 0.08 | 0.08 |
| Los Angeles | 0.16 | 0.16 | 0.15 | 0.16 | 0.15 | 0.16 | 0.16 | 0.16 | 0.16 |
| New York City | 0.13 | 0.14 | 0.14 | 0.13 | 0.14 | 0.14 | 0.14 | 0.13 | 0.14 |
| Portland | 0.07 | 0.07 | 0.07 | 0.07 | 0.06 | 0.07 | 0.07 | 0.07 | 0.07 |
| San Francisco | 0.23 | 0.24 | 0.23 | 0.24 | 0.25 | 0.24 | 0.23 | 0.24 | 0.24 |
| Seattle | 0.09 | 0.08 | 0.09 | 0.08 | 0.08 | 0.08 | 0.08 | 0.08 | 0.08 |
| All | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
def dm(x):
if x >=1:
return 1
else :
return 0
basket = df.groupby(["Order ID","Product"]).apply(len).unstack()
basket.fillna(0,inplace=True)
basket = basket.applymap(dm)
basket.describe()
| Product | 20in Monitor | 27in 4K Gaming Monitor | 27in FHD Monitor | 34in Ultrawide Monitor | AA Batteries (4-pack) | AAA Batteries (4-pack) | Apple Airpods Headphones | Bose SoundSport Headphones | Flatscreen TV | Google Phone | LG Dryer | LG Washing Machine | Lightning Charging Cable | Macbook Pro Laptop | ThinkPad Laptop | USB-C Charging Cable | Vareebadd Phone | Wired Headphones | iPhone |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 178437.000000 | 178437.000000 | 178437.000000 | 178437.000000 | 178437.000000 | 178437.000000 | 178437.000000 | 178437.000000 | 178437.000000 | 178437.000000 | 178437.00000 | 178437.000000 | 178437.000000 | 178437.000000 | 178437.000000 | 178437.000000 | 178437.000000 | 178437.000000 | 178437.000000 |
| mean | 0.022966 | 0.034886 | 0.042020 | 0.034600 | 0.115122 | 0.115408 | 0.087005 | 0.074525 | 0.026867 | 0.030946 | 0.00362 | 0.003732 | 0.121074 | 0.026458 | 0.023123 | 0.122480 | 0.011573 | 0.105623 | 0.038333 |
| std | 0.149796 | 0.183492 | 0.200636 | 0.182766 | 0.319170 | 0.319514 | 0.281844 | 0.262624 | 0.161694 | 0.173173 | 0.06006 | 0.060980 | 0.326214 | 0.160492 | 0.150295 | 0.327841 | 0.106953 | 0.307355 | 0.191999 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
basket['total'] = basket.sum(axis=1)
basket = basket.query("total > 1")
basket.drop(columns = 'total',inplace=True)
basket = basket.replace([0,1],[False ,True])
basket
| Product | 20in Monitor | 27in 4K Gaming Monitor | 27in FHD Monitor | 34in Ultrawide Monitor | AA Batteries (4-pack) | AAA Batteries (4-pack) | Apple Airpods Headphones | Bose SoundSport Headphones | Flatscreen TV | Google Phone | LG Dryer | LG Washing Machine | Lightning Charging Cable | Macbook Pro Laptop | ThinkPad Laptop | USB-C Charging Cable | Vareebadd Phone | Wired Headphones | iPhone |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Order ID | |||||||||||||||||||
| 141275 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | True | False |
| 141290 | False | False | False | False | True | False | True | False | False | False | False | False | False | False | False | False | False | False | False |
| 141365 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True | False |
| 141384 | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | True | False | False | False |
| 141450 | False | False | False | False | False | False | False | True | False | True | False | False | False | False | False | False | False | False | False |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 319536 | False | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | True | False |
| 319556 | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False | False | True | False |
| 319584 | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | False | True | True |
| 319596 | False | False | False | False | False | False | False | False | False | False | False | False | True | False | False | False | False | False | True |
| 319631 | False | False | False | True | False | False | False | False | False | False | False | False | True | False | False | False | False | False | False |
6832 rows × 19 columns
from mlxtend.frequent_patterns import fpgrowth ,association_rules
model = fpgrowth(df = basket ,min_support=0.05 ,use_colnames=True,verbose=10)
10 itemset(s) from tree conditioned on items () 0 itemset(s) from tree conditioned on items (USB-C Charging Cable) 2 itemset(s) from tree conditioned on items (Wired Headphones) 0 itemset(s) from tree conditioned on items (Wired Headphones, iPhone) 0 itemset(s) from tree conditioned on items (Wired Headphones, Google Phone) 1 itemset(s) from tree conditioned on items (Apple Airpods Headphones) 0 itemset(s) from tree conditioned on items (AA Batteries (4-pack)) 1 itemset(s) from tree conditioned on items (Vareebadd Phone) 1 itemset(s) from tree conditioned on items (Google Phone) 0 itemset(s) from tree conditioned on items (Bose SoundSport Headphones) 0 itemset(s) from tree conditioned on items (iPhone) 1 itemset(s) from tree conditioned on items (Lightning Charging Cable) 0 itemset(s) from tree conditioned on items (AAA Batteries (4-pack))
model
| support | itemsets | |
|---|---|---|
| 0 | 0.295228 | (USB-C Charging Cable) |
| 1 | 0.234924 | (Wired Headphones) |
| 2 | 0.135539 | (Apple Airpods Headphones) |
| 3 | 0.102166 | (AA Batteries (4-pack)) |
| 4 | 0.087968 | (Vareebadd Phone) |
| 5 | 0.239022 | (Google Phone) |
| 6 | 0.112119 | (Bose SoundSport Headphones) |
| 7 | 0.272834 | (iPhone) |
| 8 | 0.252049 | (Lightning Charging Cable) |
| 9 | 0.105240 | (AAA Batteries (4-pack)) |
| 10 | 0.067623 | (iPhone, Wired Headphones) |
| 11 | 0.061768 | (Wired Headphones, Google Phone) |
| 12 | 0.054596 | (iPhone, Apple Airpods Headphones) |
| 13 | 0.053864 | (USB-C Charging Cable, Vareebadd Phone) |
| 14 | 0.145931 | (USB-C Charging Cable, Google Phone) |
| 15 | 0.147980 | (iPhone, Lightning Charging Cable) |
import MarketBaksetNetwork
from MarketBaksetNetwork import draw_network
relations = association_rules(df = model ,metric='lift' ,min_threshold=1)
relations = relations.query("confidence > 0.5")
relations
| antecedents | consequents | antecedent support | consequent support | support | confidence | lift | leverage | conviction | zhangs_metric | |
|---|---|---|---|---|---|---|---|---|---|---|
| 7 | (Vareebadd Phone) | (USB-C Charging Cable) | 0.087968 | 0.295228 | 0.053864 | 0.612313 | 2.074031 | 0.027893 | 1.817887 | 0.567795 |
| 9 | (Google Phone) | (USB-C Charging Cable) | 0.239022 | 0.295228 | 0.145931 | 0.610533 | 2.068002 | 0.075365 | 1.809579 | 0.678655 |
| 10 | (iPhone) | (Lightning Charging Cable) | 0.272834 | 0.252049 | 0.147980 | 0.542382 | 2.151889 | 0.079213 | 1.634444 | 0.736134 |
| 11 | (Lightning Charging Cable) | (iPhone) | 0.252049 | 0.272834 | 0.147980 | 0.587108 | 2.151889 | 0.079213 | 1.761154 | 0.715678 |
plt.subplots(figsize=(14,8))
draw_network(relations ,rules_to_show=3)
df['Order Date2']=df['Order Date'].dt.to_period('D')
time_series = df.groupby("Order Date2")["Price Each"].sum()
plt.subplots(figsize=(14,8))
time_series.plot()
sns.despine()
plt.show()
import RootsTimeSeries
from RootsTimeSeries import CharacteristicPlotter
from scipy.stats import shapiro
from statsmodels.stats.stattools import durbin_watson
from statsmodels.stats.diagnostic import acorr_ljungbox
from statsmodels.stats.diagnostic import het_white
from statsmodels.graphics.gofplots import qqplot
import statsmodels
import pmdarima
import skforecast
import sklearn
from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf,plot_pacf
from statsmodels.tsa.arima.model import ARIMA
from pmdarima import auto_arima
from skforecast.ForecasterAutoreg import ForecasterAutoreg
from skforecast.ForecasterAutoregDirect import ForecasterAutoregDirect
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
if adfuller(time_series)[1] <= 0.05:
print(f"stationray:{adfuller(time_series)[1]}")
else :
print("non stationray")
non stationray
if adfuller(time_series.diff().dropna())[1] <= 0.05:
print(f"stationray:{adfuller(time_series.diff().dropna())[1]}")
else :
print("non stationray")
stationray:7.142264907245654e-23
ax,(ax ,ax1) = plt.subplots(nrows=1,ncols=2,figsize=(14,8))
plot_pacf(time_series.diff().dropna(),ax=ax)
plot_acf(time_series.diff().dropna(),ax=ax1)
sns.despine()
AR(1) ,MA(1)
time_series.index=time_series.index.to_timestamp()
# split train test cv
_ ,X_test = train_test_split(time_series ,train_size=0.8 ,test_size=0.2 ,shuffle=False)
X_train ,X_cv = train_test_split(_ ,train_size=0.8 ,test_size=0.2 ,shuffle=False)
# fit the mode
model = ARIMA(endog=X_train ,order=(1,0,1)).fit()
plt.subplots(figsize=(14,8))
X_train.plot(label='train')
model.predict().plot(label='forecast')
sns.despine()
plt.show()
# train score
train_score = r2_score(X_train ,model.predict())
train_score
0.7123195311128949
X_train1 = X_train.copy()
X_train2 = X_train.copy()
# cv score Walk Forward Method
predictions = []
for _ in range(len(X_cv)):
X_train1 = X_train1.append(X_cv.iloc[_:_+1])
model1 = ARIMA(endog=X_train1 ,order=(1,0,1)).fit()
predictions.append(model1.forecast().values[0])
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
# test score Walk Forward Method
predictions1 = []
for _ in range(len(X_test)):
X_train2 = X_train2.append(X_test.iloc[_:_+1])
model2 = ARIMA(endog=X_train2 ,order=(1,0,1)).fit()
predictions1.append(model2.forecast().values[0])
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
warn('Non-stationary starting autoregressive parameters'
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
self._init_dates(dates, freq)
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
return get_prediction_index(
train_score,r2_score(X_cv ,predictions),r2_score(X_test ,predictions1)
(0.7123195311128949, 0.8601769713874752, 0.681992099389361)
fig, (ax, ax1 ,ax2) = plt.subplots(nrows=1, ncols=3, figsize=(14, 8))
# Plotting on the first subplot
sns.lineplot(x=X_test.index, y=X_test.values, ax=ax2, label='test')
sns.lineplot(x=X_test.index, y=predictions1, ax=ax2, label='Predicted')
# Plotting on the second subplot
sns.lineplot(x=X_cv.index, y=X_cv.values, ax=ax1, label='cv')
sns.lineplot(x=X_cv.index, y=predictions, ax=ax1, label='Predicted')
# Plotting on the second subplot
sns.lineplot(x=X_train.index, y=X_train.values, ax=ax, label='train')
sns.lineplot(x=X_train.index, y=model.predict().values, ax=ax, label='Predicted')
# Adding legends
ax1.legend()
ax2.legend()
ax.legend()
sns.despine()
ax.tick_params(axis='x', rotation=90)
ax1.tick_params(axis='x', rotation=90)
ax2.tick_params(axis='x', rotation=90)
# Display the plots
plt.show()
model_auto = auto_arima(y = X_train ,start_p=1 ,start_q= 1 ,max_p= 3 ,max_q=3 ,seasonal=False)
model_auto
ARIMA(0,1,1)(0,0,0)[0]In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
ARIMA(0,1,1)(0,0,0)[0]
# new model
# fit the mode
X_train3 = X_train.copy()
X_train4 = X_train.copy()
new_model = ARIMA(endog=X_train ,order=(0,1,1)).fit()
# cv score Walk Forward Method
predictions2 = []
for _ in range(len(X_cv)):
X_train3 = X_train3.append(X_cv.iloc[_:_+1])
new_model1 = ARIMA(endog=X_train3 ,order=(0,1,1)).fit()
predictions2.append(new_model1.forecast().values[0])
# test score Walk Forward Method
predictions3 = []
for _ in range(len(X_test)):
X_train4 = X_train4.append(X_test.iloc[_:_+1])
new_model2 = ARIMA(endog=X_train4 ,order=(0,1,1)).fit()
predictions3.append(new_model2.forecast().values[0])
C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index( C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting. self._init_dates(dates, freq) C:\Users\HP\AppData\Local\Programs\Python\Python310\lib\site-packages\statsmodels\tsa\base\tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`. return get_prediction_index(
r2_score(X_train ,new_model.predict()),r2_score(X_cv ,predictions2),r2_score(X_test ,predictions3)
(0.6461331426790304, 0.8634950082876237, 0.6777327275799437)
this model do better on generalization than first model
fig, (ax, ax1 ,ax2) = plt.subplots(nrows=1, ncols=3, figsize=(14, 8))
# Plotting on the first subplot
sns.lineplot(x=X_test.index, y=X_test.values, ax=ax2, label='test')
sns.lineplot(x=X_test.index, y=predictions3, ax=ax2, label='Predicted')
# Plotting on the second subplot
sns.lineplot(x=X_cv.index, y=X_cv.values, ax=ax1, label='cv')
sns.lineplot(x=X_cv.index, y=predictions2, ax=ax1, label='Predicted')
# Plotting on the second subplot
sns.lineplot(x=X_train.index, y=X_train.values, ax=ax, label='train')
sns.lineplot(x=X_train.index, y=new_model.predict().values, ax=ax, label='Predicted')
# Adding legends
ax1.legend()
ax2.legend()
ax.legend()
sns.despine()
ax.tick_params(axis='x', rotation=90)
ax1.tick_params(axis='x', rotation=90)
ax2.tick_params(axis='x', rotation=90)
# Display the plots
plt.show()
fig, (ax, ax1 ,ax2) = plt.subplots(nrows=1, ncols=3, figsize=(14, 8))
# Plotting on the first subplot
sns.lineplot(x=X_test.index, y=X_test.values, ax=ax2, label='test')
sns.lineplot(x=X_test.index, y=predictions1, ax=ax2, label='Predicted')
# Plotting on the second subplot
sns.lineplot(x=X_cv.index, y=X_cv.values, ax=ax1, label='cv')
sns.lineplot(x=X_cv.index, y=predictions, ax=ax1, label='Predicted')
# Plotting on the second subplot
sns.lineplot(x=X_train.index, y=X_train.values, ax=ax, label='train')
sns.lineplot(x=X_train.index, y=model.predict().values, ax=ax, label='Predicted')
# Adding legends
ax1.legend()
ax2.legend()
ax.legend()
sns.despine()
ax.tick_params(axis='x', rotation=90)
ax1.tick_params(axis='x', rotation=90)
ax2.tick_params(axis='x', rotation=90)
# Display the plots
plt.show()
import RootsTimeSeries
from RootsTimeSeries import CharacteristicPlotter
from scipy.stats import shapiro
from statsmodels.stats.stattools import durbin_watson
from statsmodels.stats.diagnostic import acorr_ljungbox
from statsmodels.stats.diagnostic import het_white ,het_arch
from statsmodels.graphics.gofplots import qqplot
model.params[2]
(0.9918325978063867, -0.7525016041380406)
params = [[model.params[1]],[model.params[2]]]
checks1 = CharacteristicPlotter(coef=params ,ar_ma='ar_ma')
stationary : True invertible : True
checks1.plot_roots()
shapiro(model.resid)[1],shapiro(new_model.resid)[1]
(0.4571443796157837, 4.6702254508090846e-08)
fig,(ax,ax1)=plt.subplots(nrows=2,ncols=2,figsize=(14,8))
sns.histplot(model.resid,ax=ax[0] ,label='model')
sns.histplot(new_model.resid,ax=ax1[0],label='model2')
qqplot(model.resid,ax=ax[1] ,label='model',line='s')
qqplot(new_model1.resid,ax=ax1[1],label='model2',line='s')
sns.despine()
ax[0].legend()
ax1[0].legend()
plt.show()
if durbin_watson(model.resid) <= 2.1 and durbin_watson(model.resid) >= 1.9 :
print('no first order autocorrelation')
else:
print('first order autocorrelation')
no first order autocorrelation
if durbin_watson(new_model.resid) <= 2.1 and durbin_watson(new_model.resid) >= 1.9 :
print('no first order autocorrelation')
else:
print('first order autocorrelation')
first order autocorrelation
acorr_ljungbox(model.resid,lags=5)['lb_pvalue'].values,acorr_ljungbox(new_model.resid,lags=5)['lb_pvalue'].values
(array([0.6642226 , 0.85085964, 0.70492698, 0.84373753, 0.92402212]), array([0.3414813 , 0.55313309, 0.42846029, 0.57551609, 0.71349413]))
# forecast 7 points using recursive method
new_model_final = ARIMA(endog=time_series ,order=(0,1,1)).fit()
time_series.append(new_model_final.forecast(steps=7))
2019-01-01 65420.910000
2019-01-02 70407.840000
2019-01-03 46793.740000
2019-01-04 61779.510000
2019-01-05 46160.620000
...
2020-01-04 92151.573015
2020-01-05 92151.573015
2020-01-06 92151.573015
2020-01-07 92151.573015
2020-01-08 92151.573015
Freq: D, Length: 373, dtype: float64
plt.subplots(figsize=(14,8))
plt.plot(time_series[-7:])
plt.plot(time_series.append(new_model_final.forecast(steps=7))[-14:],'--')
sns.despine()
plt.title("Forecasting next 4 weeks")
plt.show()
for _ in range(1,150,1):
mod = ForecasterAutoregDirect(regressor=LinearRegression() ,steps = 7 ,lags=_)
mod.fit(X_train)
print(r2_score(X_cv.iloc[:7].values,mod.predict().values),_)
-2.151254054856309 1 -0.42260733390226757 2 -0.13729987215929462 3 -0.31226929475993925 4 -0.2507792317684303 5 -0.25702278775503307 6 -0.1566357883821632 7 -0.14354521125239939 8 -0.08719138136947446 9 -0.0628755302165851 10 -0.007681173672709152 11 -0.40452355662884343 12 -0.3732350878556232 13 -0.42544533931136597 14 -0.42771074908205753 15 -0.500343355197421 16 -0.512845170459588 17 -0.424909540339804 18 -0.3755096542251595 19 -0.4598393971152801 20 -0.45581065099983564 21 -0.61198474312898 22 -0.6974699100544388 23 -0.7280709176620441 24 -0.8049961379233492 25 -0.7612786017898687 26 -0.6781809945300699 27 -0.6026298326962067 28 -0.6348594972144164 29 -0.5337880631531913 30 -0.7255977426492293 31 -0.934099041587364 32 -1.2200240123601662 33 -1.147864914371251 34 -1.135066715030392 35 -1.2721067942577218 36 -1.2781015707742251 37 -1.3067300106421063 38 -1.2416161696843284 39 -1.391612467603264 40 -1.2596775418569757 41 -1.4256234507951207 42 -1.390763770081274 43 -1.3912970121344261 44 -1.6349453185537897 45 -1.716594461667631 46 -1.479544516829876 47 -1.3785205658144886 48 -1.551073867780811 49 -1.5506176850980387 50 -0.8744407032883301 51 -1.008251961117005 52 -0.7902531381658022 53 -0.6758490759646287 54 -0.82257639356966 55 -0.5400694756524584 56 -0.6212688068923788 57 -0.6946875746424783 58 -0.908459406129208 59 -0.9518422608254473 60 -0.8218319590474978 61 -0.7779160444271889 62 -0.8378247481016472 63 -0.7843778996102237 64 -0.7343758201970401 65 -0.7147558752529444 66 -0.6779162072919862 67 -0.671071989135114 68 -0.5299213736001762 69 -0.5725149441065509 70 -0.5586273457323445 71 -0.5635616511445993 72 -1.4244897555526603 73 -0.6806895191280447 74 -0.49000940938256865 75 -1.497026227630176 76 -1.564960685032064 77 -1.5364645209562635 78 -1.5213522149540624 79 -1.4738221616284646 80 -1.5529908935351626 81 -1.493902544719159 82 -1.1881731509471898 83 -0.5017893943959562 84 -0.507472016778586 85 -0.530311025406655 86 -0.984492784519956 87 -1.0540382872950613 88 -1.3116495638575354 89 -1.209816203307854 90 -1.456424392710884 91 -2.4103215863396876 92 -3.42191221321743 93 -3.5900967359931126 94 -3.72076354789489 95 -4.23009000199252 96 -4.6453817009155935 97 -7.443651544225563 98 -6.129267138287477 99 -6.539999510863717 100 -9.744576678932019 101 -7.21566114050427 102 -12.013854055540648 103 -12.847897699458974 104 -13.615564286246444 105 -14.274078137110775 106 -19.86623835699873 107 -19.93521472384528 108 -31.130453600040724 109 -38.118181683541216 110 -85.06339040086624 111 -163.50288705467068 112 -1071.4918569400504 113 -21.75833417276669 114 -9.57841038582858 115 -18.159578763823042 116 -11.954496698519446 117 -17.4045626345819 118 -13.586236481600439 119 -11.070973941923464 120 -9.621552459408294 121 -13.742399965746792 122 -15.921239927562048 123 -15.632819591673375 124 -8.616536171112916 125 -5.4925892328732555 126 -9.111153483049769 127 -4.191810149250979 128 -3.8008959312523283 129 -3.434710984758615 130 -1.958714167091924 131 -1.8116215687798798 132 -2.3028120105570755 133 -0.9322457123703407 134 -1.114182859880008 135 -1.0439759222373657 136 -0.4069621943545241 137 -0.38664304698900964 138 -0.7861031572831256 139 -0.9298481340418989 140 -0.5648647708072265 141 -0.2144021322062568 142 -0.2800700968204075 143 -0.2861339492235089 144 -0.29422321849086 145 -0.10184780344324484 146 -0.48068579739386985 147 -0.5011206106835073 148 -0.5908880330529904 149
mod = ForecasterAutoregDirect(regressor=LinearRegression() ,steps = 7 ,lags=12)
mod.fit(X_train)
X_cv.iloc[:7].plot()
mod.predict().plot()
<AxesSubplot:xlabel='Order Date2'>
this method isn't good ,i can use non linear model to capture more complex relation
import lightgbm
import xgboost
import catboost
from lightgbm import LGBMRegressor
from xgboost import XGBRegressor
from catboost import CatBoostRegressor
for _ in range(1,20,1):
mod = ForecasterAutoregDirect(regressor=LGBMRegressor(random_state=0) ,steps = 7 ,lags=_)
mod.fit(X_train)
print(r2_score(X_cv.iloc[:7].values,mod.predict().values),_)
-2.605092634271937 1 -0.590586794170475 2 0.30284228012024617 3 -0.17571585634025277 4 -0.14787494078216357 5 -0.3483119058843649 6 -0.3223297303387971 7 -0.0822019061889343 8 0.14212159083446008 9 0.2792531738702769 10 0.03436860402601938 11 -0.14045345717596924 12 -0.3008839671997221 13 -0.2619015186858109 14 0.1841655008048435 15 -0.16333372565882498 16 0.05778642260367772 17 -0.36563928177340443 18 -0.07165292320937078 19
for _ in range(1,20,1):
mod = ForecasterAutoregDirect(regressor=XGBRegressor(random_state=0) ,steps = 7 ,lags=_)
mod.fit(X_train)
print(r2_score(X_cv.iloc[:7].values,mod.predict().values),_)
-4.683889161863324 1 -2.1698883695813134 2 -1.8007475652170366 3 -0.9717920789410659 4 -0.8654299340742153 5 -0.15954545914923535 6 -1.272109457146267 7 0.29386573867806176 8 -0.13066636604543191 9 0.47967224372468653 10 0.5188539870011358 11 -0.32414254039842527 12 -0.25267380382320725 13 -0.5455551602734832 14 -0.1994447674365698 15 -0.9590458671668733 16 -0.7264633107128671 17 -0.7834277585951652 18 -0.07719442304788982 19
for _ in range(1,20,1):
mod = ForecasterAutoregDirect(regressor=CatBoostRegressor(random_state=0) ,steps = 7 ,lags=_)
mod.fit(X_train)
print(r2_score(X_cv.iloc[:7].values,mod.predict().values),_)
-4.176834933936596 1 -1.6600386500110926 2 -0.3566079123118393 3 -0.14008101947779728 4 -0.018524897271272378 5 0.022164404829809303 6 -0.054877116791302916 7 -0.36061452869335486 8 -0.05256705833257702 9 -0.03740795722274326 10 -0.08860242345642244 11 -0.5253673936846301 12 -0.5024780217323075 13 -0.9161020993339637 14 -0.8066366794294093 15 -0.6556217795503541 16 -0.5057646645903662 17 -0.4259435314376747 18 -0.4026432605123147 19
m = X_train.max()
Xt = X_train/m
Xc = X_cv/m
for _ in range(1,150,1):
mod = ForecasterAutoregDirect(regressor=LinearRegression() ,steps = 7 ,lags=_)
mod.fit(Xt)
print(r2_score(Xc.iloc[:7].values,mod.predict().values),_)
Xc.iloc[:7].plot()
mod.predict().plot()
plt.show()
-2.151254054856313 1
-0.42260733390226424 2
-0.13729987215929773 3
-0.3122692947599419 4
-0.25077923176843275 5
-0.25702278775502996 6
-0.15663578838216563 7
-0.14354521125239983 8
-0.08719138136947446 9
-0.06287553021658399 10
-0.007681173672710262 11
-0.40452355662884676 12
-0.37323508785562765 13
-0.4254453393113655 14
-0.42771074908205287 15
-0.5003433551974188 16
-0.5128451704595891 17
-0.42490954033980644 18
-0.3755096542251597 19
-0.45983939711527433 20
-0.4558106509998352 21
-0.6119847431289789 22
-0.6974699100544401 23
-0.7280709176620481 24
-0.804996137923343 25
-0.7612786017898607 26
-0.6781809945300623 27
-0.6026298326962074 28
-0.6348594972144177 29
-0.533788063153189 30
-0.725597742649229 31
-0.9340990415873653 32
-1.2200240123601644 33
-1.147864914371246 34
-1.1350667150303932 35
-1.2721067942577196 36
-1.2781015707742225 37
-1.3067300106421063 38
-1.241616169684331 39
-1.391612467603275 40
-1.259677541856988 41
-1.4256234507951278 42
-1.3907637700812878 43
-1.3912970121344403 44
-1.6349453185537994 45
-1.7165944616676456 46
-1.479544516829888 47
-1.3785205658144957 48
-1.5510738677808202 49
-1.5506176850980684 50
-0.8744407032883419 51
-1.00825196111701 52
-0.7902531381658122 53
-0.6758490759646378 54
-0.8225763935696682 55
-0.5400694756524587 56
-0.621268806892384 57
-0.6946875746424761 58
-0.9084594061292115 59
-0.9518422608254486 60
-0.8218319590475118 61
-0.7779160444271929 62
-0.8378247481016485 63
-0.7843778996102235 64
-0.7343758201970343 65
-0.7147558752529373 66
-0.6779162072919851 67
-0.6710719891351049 68
-0.5299213736001764 69
-0.5725149441065509 70
-0.5586273457323405 71
-0.5635616511446007 72
-1.4244897555526412 73
-0.6806895191280442 74
-0.4900094093825509 75
-1.497026227630153 76
-1.5649606850320588 77
-1.5364645209562457 78
-1.5213522149540522 79
-1.4738221616284481 80
-1.552990893535155 81
-1.4939025447191332 82
-1.1881731509471711 83
-0.501789394395945 84
-0.5074720167785964 85
-0.5303110254066565 86
-0.9844927845199527 87
-1.0540382872950689 88
-1.311649563857519 89
-1.209816203307852 90
-1.4564243927108778 91
-2.4103215863397383 92
-3.4219122132174506 93
-3.59009673599311 94
-3.7207635478949124 95
-4.230090001992562 96
-4.64538170091564 97
-7.443651544225629 98
-6.12926713828753 99
-6.5399995108637246 100
-9.744576678932058 101
-7.215661140504297 102
-12.013854055540698 103
-12.847897699458985 104
-13.61556428624643 105
-14.274078137110697 106
-19.866238356998892 107
-19.93521472384538 108
-31.130453600040674 109
-38.11818168354148 110
-85.06339040086695 111
-163.50288705467136 112
-1071.4918569400488 113
-56.224907466655715 114
-17.37806460730822 115
-21.2332521722473 116
-12.014015792555211 117
-17.074466648449103 118
-13.045427239994964 119
-11.56430658725896 120
-9.761203744537807 121
-13.532873079620645 122
-15.646320878221285 123
-16.43038893623468 124
-8.268514508314116 125
-5.3907856329858514 126
-7.622182310031295 127
-3.7099921572206034 128
-4.453031262198642 129
-3.6203795510962227 130
-1.8130855389095202 131
-1.6498036911663276 132
-2.1322459822202107 133
-0.9943377061300904 134
-0.926722323709837 135
-0.9448711130814862 136
-0.6046240926179218 137
-0.5054381990730934 138
-1.0904821034134673 139
-0.8697724018753259 140
-0.7698029055412476 141
-0.2379946216518356 142
-0.3432292480050749 143
-0.4001308160464865 144
-0.4075688812743181 145
-0.27814692482441794 146
-0.4597536412302001 147
-0.4175512431029247 148
-0.5906353524760526 149
x=[]
y=[]
for _ in range(len(time_series)-13):
print(time_series.values[_:_+7],0,time_series.values[_+7:_+14])
x.append(time_series.values[_:_+7]),y.append(time_series.values[_+7:_+14])
[65420.91 70407.84 46793.74 61779.51 46160.62 52500.25 53493.66] 0 [55868.09 54944.27 56114.35 78304.82 47792.25 61021.67 49844.91] [70407.84 46793.74 61779.51 46160.62 52500.25 53493.66 55868.09] 0 [54944.27 56114.35 78304.82 47792.25 61021.67 49844.91 63988.26] [46793.74 61779.51 46160.62 52500.25 53493.66 55868.09 54944.27] 0 [56114.35 78304.82 47792.25 61021.67 49844.91 63988.26 51065.39] [61779.51 46160.62 52500.25 53493.66 55868.09 54944.27 56114.35] 0 [78304.82 47792.25 61021.67 49844.91 63988.26 51065.39 54931.56] [46160.62 52500.25 53493.66 55868.09 54944.27 56114.35 78304.82] 0 [47792.25 61021.67 49844.91 63988.26 51065.39 54931.56 48291.98] [52500.25 53493.66 55868.09 54944.27 56114.35 78304.82 47792.25] 0 [61021.67 49844.91 63988.26 51065.39 54931.56 48291.98 56709.41] [53493.66 55868.09 54944.27 56114.35 78304.82 47792.25 61021.67] 0 [49844.91 63988.26 51065.39 54931.56 48291.98 56709.41 67913.55] [55868.09 54944.27 56114.35 78304.82 47792.25 61021.67 49844.91] 0 [63988.26 51065.39 54931.56 48291.98 56709.41 67913.55 60655.77] [54944.27 56114.35 78304.82 47792.25 61021.67 49844.91 63988.26] 0 [51065.39 54931.56 48291.98 56709.41 67913.55 60655.77 59216.85] [56114.35 78304.82 47792.25 61021.67 49844.91 63988.26 51065.39] 0 [54931.56 48291.98 56709.41 67913.55 60655.77 59216.85 57046.3 ] [78304.82 47792.25 61021.67 49844.91 63988.26 51065.39 54931.56] 0 [48291.98 56709.41 67913.55 60655.77 59216.85 57046.3 55640.48] [47792.25 61021.67 49844.91 63988.26 51065.39 54931.56 48291.98] 0 [56709.41 67913.55 60655.77 59216.85 57046.3 55640.48 57405.81] [61021.67 49844.91 63988.26 51065.39 54931.56 48291.98 56709.41] 0 [67913.55 60655.77 59216.85 57046.3 55640.48 57405.81 71063.06] [49844.91 63988.26 51065.39 54931.56 48291.98 56709.41 67913.55] 0 [60655.77 59216.85 57046.3 55640.48 57405.81 71063.06 63490.03] [63988.26 51065.39 54931.56 48291.98 56709.41 67913.55 60655.77] 0 [59216.85 57046.3 55640.48 57405.81 71063.06 63490.03 56816.99] [51065.39 54931.56 48291.98 56709.41 67913.55 60655.77 59216.85] 0 [57046.3 55640.48 57405.81 71063.06 63490.03 56816.99 61511.91] [54931.56 48291.98 56709.41 67913.55 60655.77 59216.85 57046.3 ] 0 [55640.48 57405.81 71063.06 63490.03 56816.99 61511.91 56051.97] [48291.98 56709.41 67913.55 60655.77 59216.85 57046.3 55640.48] 0 [57405.81 71063.06 63490.03 56816.99 61511.91 56051.97 61018.77] [56709.41 67913.55 60655.77 59216.85 57046.3 55640.48 57405.81] 0 [71063.06 63490.03 56816.99 61511.91 56051.97 61018.77 70921.79] [67913.55 60655.77 59216.85 57046.3 55640.48 57405.81 71063.06] 0 [63490.03 56816.99 61511.91 56051.97 61018.77 70921.79 77481.89] [60655.77 59216.85 57046.3 55640.48 57405.81 71063.06 63490.03] 0 [56816.99 61511.91 56051.97 61018.77 70921.79 77481.89 72760.09] [59216.85 57046.3 55640.48 57405.81 71063.06 63490.03 56816.99] 0 [61511.91 56051.97 61018.77 70921.79 77481.89 72760.09 69913.22] [57046.3 55640.48 57405.81 71063.06 63490.03 56816.99 61511.91] 0 [56051.97 61018.77 70921.79 77481.89 72760.09 69913.22 75666.94] [55640.48 57405.81 71063.06 63490.03 56816.99 61511.91 56051.97] 0 [61018.77 70921.79 77481.89 72760.09 69913.22 75666.94 86074.8 ] [57405.81 71063.06 63490.03 56816.99 61511.91 56051.97 61018.77] 0 [70921.79 77481.89 72760.09 69913.22 75666.94 86074.8 73948.86] [71063.06 63490.03 56816.99 61511.91 56051.97 61018.77 70921.79] 0 [77481.89 72760.09 69913.22 75666.94 86074.8 73948.86 93573.76] [63490.03 56816.99 61511.91 56051.97 61018.77 70921.79 77481.89] 0 [72760.09 69913.22 75666.94 86074.8 73948.86 93573.76 87510.93] [56816.99 61511.91 56051.97 61018.77 70921.79 77481.89 72760.09] 0 [69913.22 75666.94 86074.8 73948.86 93573.76 87510.93 76639.43] [61511.91 56051.97 61018.77 70921.79 77481.89 72760.09 69913.22] 0 [75666.94 86074.8 73948.86 93573.76 87510.93 76639.43 74835.85] [56051.97 61018.77 70921.79 77481.89 72760.09 69913.22 75666.94] 0 [86074.8 73948.86 93573.76 87510.93 76639.43 74835.85 80789.87] [61018.77 70921.79 77481.89 72760.09 69913.22 75666.94 86074.8 ] 0 [73948.86 93573.76 87510.93 76639.43 74835.85 80789.87 92087.09] [70921.79 77481.89 72760.09 69913.22 75666.94 86074.8 73948.86] 0 [93573.76 87510.93 76639.43 74835.85 80789.87 92087.09 71390.05] [77481.89 72760.09 69913.22 75666.94 86074.8 73948.86 93573.76] 0 [87510.93 76639.43 74835.85 80789.87 92087.09 71390.05 75294.56] [72760.09 69913.22 75666.94 86074.8 73948.86 93573.76 87510.93] 0 [76639.43 74835.85 80789.87 92087.09 71390.05 75294.56 87412.53] [69913.22 75666.94 86074.8 73948.86 93573.76 87510.93 76639.43] 0 [74835.85 80789.87 92087.09 71390.05 75294.56 87412.53 83216.47] [75666.94 86074.8 73948.86 93573.76 87510.93 76639.43 74835.85] 0 [80789.87 92087.09 71390.05 75294.56 87412.53 83216.47 79050.34] [86074.8 73948.86 93573.76 87510.93 76639.43 74835.85 80789.87] 0 [92087.09 71390.05 75294.56 87412.53 83216.47 79050.34 75334.78] [73948.86 93573.76 87510.93 76639.43 74835.85 80789.87 92087.09] 0 [71390.05 75294.56 87412.53 83216.47 79050.34 75334.78 74380.82] [93573.76 87510.93 76639.43 74835.85 80789.87 92087.09 71390.05] 0 [75294.56 87412.53 83216.47 79050.34 75334.78 74380.82 80589.78] [87510.93 76639.43 74835.85 80789.87 92087.09 71390.05 75294.56] 0 [87412.53 83216.47 79050.34 75334.78 74380.82 80589.78 81672.08] [76639.43 74835.85 80789.87 92087.09 71390.05 75294.56 87412.53] 0 [83216.47 79050.34 75334.78 74380.82 80589.78 81672.08 65520.44] [74835.85 80789.87 92087.09 71390.05 75294.56 87412.53 83216.47] 0 [79050.34 75334.78 74380.82 80589.78 81672.08 65520.44 74414.6 ] [80789.87 92087.09 71390.05 75294.56 87412.53 83216.47 79050.34] 0 [75334.78 74380.82 80589.78 81672.08 65520.44 74414.6 70799.73] [92087.09 71390.05 75294.56 87412.53 83216.47 79050.34 75334.78] 0 [74380.82 80589.78 81672.08 65520.44 74414.6 70799.73 79678.29] [71390.05 75294.56 87412.53 83216.47 79050.34 75334.78 74380.82] 0 [80589.78 81672.08 65520.44 74414.6 70799.73 79678.29 89981.19] [75294.56 87412.53 83216.47 79050.34 75334.78 74380.82 80589.78] 0 [81672.08 65520.44 74414.6 70799.73 79678.29 89981.19 67944.54] [87412.53 83216.47 79050.34 75334.78 74380.82 80589.78 81672.08] 0 [65520.44 74414.6 70799.73 79678.29 89981.19 67944.54 90382.17] [83216.47 79050.34 75334.78 74380.82 80589.78 81672.08 65520.44] 0 [74414.6 70799.73 79678.29 89981.19 67944.54 90382.17 92019.21] [79050.34 75334.78 74380.82 80589.78 81672.08 65520.44 74414.6 ] 0 [70799.73 79678.29 89981.19 67944.54 90382.17 92019.21 85040.7 ] [75334.78 74380.82 80589.78 81672.08 65520.44 74414.6 70799.73] 0 [79678.29 89981.19 67944.54 90382.17 92019.21 85040.7 88200.31] [74380.82 80589.78 81672.08 65520.44 74414.6 70799.73 79678.29] 0 [89981.19 67944.54 90382.17 92019.21 85040.7 88200.31 99455.47] [80589.78 81672.08 65520.44 74414.6 70799.73 79678.29 89981.19] 0 [67944.54 90382.17 92019.21 85040.7 88200.31 99455.47 85862.34] [81672.08 65520.44 74414.6 70799.73 79678.29 89981.19 67944.54] 0 [90382.17 92019.21 85040.7 88200.31 99455.47 85862.34 95320.47] [65520.44 74414.6 70799.73 79678.29 89981.19 67944.54 90382.17] 0 [92019.21 85040.7 88200.31 99455.47 85862.34 95320.47 91492.61] [74414.6 70799.73 79678.29 89981.19 67944.54 90382.17 92019.21] 0 [85040.7 88200.31 99455.47 85862.34 95320.47 91492.61 93939.66] [70799.73 79678.29 89981.19 67944.54 90382.17 92019.21 85040.7 ] 0 [88200.31 99455.47 85862.34 95320.47 91492.61 93939.66 82938.04] [79678.29 89981.19 67944.54 90382.17 92019.21 85040.7 88200.31] 0 [99455.47 85862.34 95320.47 91492.61 93939.66 82938.04 88294.57] [89981.19 67944.54 90382.17 92019.21 85040.7 88200.31 99455.47] 0 [85862.34 95320.47 91492.61 93939.66 82938.04 88294.57 89825.18] [67944.54 90382.17 92019.21 85040.7 88200.31 99455.47 85862.34] 0 [95320.47 91492.61 93939.66 82938.04 88294.57 89825.18 87476.48] [90382.17 92019.21 85040.7 88200.31 99455.47 85862.34 95320.47] 0 [91492.61 93939.66 82938.04 88294.57 89825.18 87476.48 81907.16] [92019.21 85040.7 88200.31 99455.47 85862.34 95320.47 91492.61] 0 [93939.66 82938.04 88294.57 89825.18 87476.48 81907.16 84080.03] [85040.7 88200.31 99455.47 85862.34 95320.47 91492.61 93939.66] 0 [82938.04 88294.57 89825.18 87476.48 81907.16 84080.03 80394.07] [88200.31 99455.47 85862.34 95320.47 91492.61 93939.66 82938.04] 0 [88294.57 89825.18 87476.48 81907.16 84080.03 80394.07 81040.66] [99455.47 85862.34 95320.47 91492.61 93939.66 82938.04 88294.57] 0 [ 89825.18 87476.48 81907.16 84080.03 80394.07 81040.66 100291.58] [85862.34 95320.47 91492.61 93939.66 82938.04 88294.57 89825.18] 0 [ 87476.48 81907.16 84080.03 80394.07 81040.66 100291.58 84829.79] [95320.47 91492.61 93939.66 82938.04 88294.57 89825.18 87476.48] 0 [ 81907.16 84080.03 80394.07 81040.66 100291.58 84829.79 86029.76] [91492.61 93939.66 82938.04 88294.57 89825.18 87476.48 81907.16] 0 [ 84080.03 80394.07 81040.66 100291.58 84829.79 86029.76 84756.39] [93939.66 82938.04 88294.57 89825.18 87476.48 81907.16 84080.03] 0 [ 80394.07 81040.66 100291.58 84829.79 86029.76 84756.39 102516.59] [82938.04 88294.57 89825.18 87476.48 81907.16 84080.03 80394.07] 0 [ 81040.66 100291.58 84829.79 86029.76 84756.39 102516.59 89495.8 ] [88294.57 89825.18 87476.48 81907.16 84080.03 80394.07 81040.66] 0 [100291.58 84829.79 86029.76 84756.39 102516.59 89495.8 78082.88] [ 89825.18 87476.48 81907.16 84080.03 80394.07 81040.66 100291.58] 0 [ 84829.79 86029.76 84756.39 102516.59 89495.8 78082.88 101101.3 ] [ 87476.48 81907.16 84080.03 80394.07 81040.66 100291.58 84829.79] 0 [ 86029.76 84756.39 102516.59 89495.8 78082.88 101101.3 98513.15] [ 81907.16 84080.03 80394.07 81040.66 100291.58 84829.79 86029.76] 0 [ 84756.39 102516.59 89495.8 78082.88 101101.3 98513.15 94906.21] [ 84080.03 80394.07 81040.66 100291.58 84829.79 86029.76 84756.39] 0 [102516.59 89495.8 78082.88 101101.3 98513.15 94906.21 91704.17] [ 80394.07 81040.66 100291.58 84829.79 86029.76 84756.39 102516.59] 0 [ 89495.8 78082.88 101101.3 98513.15 94906.21 91704.17 96563.1 ] [ 81040.66 100291.58 84829.79 86029.76 84756.39 102516.59 89495.8 ] 0 [ 78082.88 101101.3 98513.15 94906.21 91704.17 96563.1 94199.38] [100291.58 84829.79 86029.76 84756.39 102516.59 89495.8 78082.88] 0 [101101.3 98513.15 94906.21 91704.17 96563.1 94199.38 90548.6 ] [ 84829.79 86029.76 84756.39 102516.59 89495.8 78082.88 101101.3 ] 0 [ 98513.15 94906.21 91704.17 96563.1 94199.38 90548.6 112002.59] [ 86029.76 84756.39 102516.59 89495.8 78082.88 101101.3 98513.15] 0 [ 94906.21 91704.17 96563.1 94199.38 90548.6 112002.59 103580.85] [ 84756.39 102516.59 89495.8 78082.88 101101.3 98513.15 94906.21] 0 [ 91704.17 96563.1 94199.38 90548.6 112002.59 103580.85 112446.54] [102516.59 89495.8 78082.88 101101.3 98513.15 94906.21 91704.17] 0 [ 96563.1 94199.38 90548.6 112002.59 103580.85 112446.54 115737.15] [ 89495.8 78082.88 101101.3 98513.15 94906.21 91704.17 96563.1 ] 0 [ 94199.38 90548.6 112002.59 103580.85 112446.54 115737.15 113545.28] [ 78082.88 101101.3 98513.15 94906.21 91704.17 96563.1 94199.38] 0 [ 90548.6 112002.59 103580.85 112446.54 115737.15 113545.28 108623.3 ] [101101.3 98513.15 94906.21 91704.17 96563.1 94199.38 90548.6 ] 0 [112002.59 103580.85 112446.54 115737.15 113545.28 108623.3 103362.4 ] [ 98513.15 94906.21 91704.17 96563.1 94199.38 90548.6 112002.59] 0 [103580.85 112446.54 115737.15 113545.28 108623.3 103362.4 94219.83] [ 94906.21 91704.17 96563.1 94199.38 90548.6 112002.59 103580.85] 0 [112446.54 115737.15 113545.28 108623.3 103362.4 94219.83 111820.11] [ 91704.17 96563.1 94199.38 90548.6 112002.59 103580.85 112446.54] 0 [115737.15 113545.28 108623.3 103362.4 94219.83 111820.11 119968.9 ] [ 96563.1 94199.38 90548.6 112002.59 103580.85 112446.54 115737.15] 0 [113545.28 108623.3 103362.4 94219.83 111820.11 119968.9 112484.79] [ 94199.38 90548.6 112002.59 103580.85 112446.54 115737.15 113545.28] 0 [108623.3 103362.4 94219.83 111820.11 119968.9 112484.79 108895.71] [ 90548.6 112002.59 103580.85 112446.54 115737.15 113545.28 108623.3 ] 0 [103362.4 94219.83 111820.11 119968.9 112484.79 108895.71 105560.71] [112002.59 103580.85 112446.54 115737.15 113545.28 108623.3 103362.4 ] 0 [ 94219.83 111820.11 119968.9 112484.79 108895.71 105560.71 104712.73] [103580.85 112446.54 115737.15 113545.28 108623.3 103362.4 94219.83] 0 [111820.11 119968.9 112484.79 108895.71 105560.71 104712.73 109918.72] [112446.54 115737.15 113545.28 108623.3 103362.4 94219.83 111820.11] 0 [119968.9 112484.79 108895.71 105560.71 104712.73 109918.72 120279.34] [115737.15 113545.28 108623.3 103362.4 94219.83 111820.11 119968.9 ] 0 [112484.79 108895.71 105560.71 104712.73 109918.72 120279.34 125904. ] [113545.28 108623.3 103362.4 94219.83 111820.11 119968.9 112484.79] 0 [108895.71 105560.71 104712.73 109918.72 120279.34 125904. 128361.8 ] [108623.3 103362.4 94219.83 111820.11 119968.9 112484.79 108895.71] 0 [105560.71 104712.73 109918.72 120279.34 125904. 128361.8 100570.88] [103362.4 94219.83 111820.11 119968.9 112484.79 108895.71 105560.71] 0 [104712.73 109918.72 120279.34 125904. 128361.8 100570.88 96371.52] [ 94219.83 111820.11 119968.9 112484.79 108895.71 105560.71 104712.73] 0 [109918.72 120279.34 125904. 128361.8 100570.88 96371.52 119822.84] [111820.11 119968.9 112484.79 108895.71 105560.71 104712.73 109918.72] 0 [120279.34 125904. 128361.8 100570.88 96371.52 119822.84 88783.21] [119968.9 112484.79 108895.71 105560.71 104712.73 109918.72 120279.34] 0 [125904. 128361.8 100570.88 96371.52 119822.84 88783.21 111308.25] [112484.79 108895.71 105560.71 104712.73 109918.72 120279.34 125904. ] 0 [128361.8 100570.88 96371.52 119822.84 88783.21 111308.25 135791.18] [108895.71 105560.71 104712.73 109918.72 120279.34 125904. 128361.8 ] 0 [100570.88 96371.52 119822.84 88783.21 111308.25 135791.18 123723.7 ] [105560.71 104712.73 109918.72 120279.34 125904. 128361.8 100570.88] 0 [ 96371.52 119822.84 88783.21 111308.25 135791.18 123723.7 114603.84] [104712.73 109918.72 120279.34 125904. 128361.8 100570.88 96371.52] 0 [119822.84 88783.21 111308.25 135791.18 123723.7 114603.84 120092.86] [109918.72 120279.34 125904. 128361.8 100570.88 96371.52 119822.84] 0 [ 88783.21 111308.25 135791.18 123723.7 114603.84 120092.86 106868.43] [120279.34 125904. 128361.8 100570.88 96371.52 119822.84 88783.21] 0 [111308.25 135791.18 123723.7 114603.84 120092.86 106868.43 121246.86] [125904. 128361.8 100570.88 96371.52 119822.84 88783.21 111308.25] 0 [135791.18 123723.7 114603.84 120092.86 106868.43 121246.86 117062.7 ] [128361.8 100570.88 96371.52 119822.84 88783.21 111308.25 135791.18] 0 [123723.7 114603.84 120092.86 106868.43 121246.86 117062.7 107363.03] [100570.88 96371.52 119822.84 88783.21 111308.25 135791.18 123723.7 ] 0 [114603.84 120092.86 106868.43 121246.86 117062.7 107363.03 97094. ] [ 96371.52 119822.84 88783.21 111308.25 135791.18 123723.7 114603.84] 0 [120092.86 106868.43 121246.86 117062.7 107363.03 97094. 105566.89] [119822.84 88783.21 111308.25 135791.18 123723.7 114603.84 120092.86] 0 [106868.43 121246.86 117062.7 107363.03 97094. 105566.89 104058.88] [ 88783.21 111308.25 135791.18 123723.7 114603.84 120092.86 106868.43] 0 [121246.86 117062.7 107363.03 97094. 105566.89 104058.88 109547.09] [111308.25 135791.18 123723.7 114603.84 120092.86 106868.43 121246.86] 0 [117062.7 107363.03 97094. 105566.89 104058.88 109547.09 95839.12] [135791.18 123723.7 114603.84 120092.86 106868.43 121246.86 117062.7 ] 0 [107363.03 97094. 105566.89 104058.88 109547.09 95839.12 94469.88] [123723.7 114603.84 120092.86 106868.43 121246.86 117062.7 107363.03] 0 [ 97094. 105566.89 104058.88 109547.09 95839.12 94469.88 107520.32] [114603.84 120092.86 106868.43 121246.86 117062.7 107363.03 97094. ] 0 [105566.89 104058.88 109547.09 95839.12 94469.88 107520.32 108840.35] [120092.86 106868.43 121246.86 117062.7 107363.03 97094. 105566.89] 0 [104058.88 109547.09 95839.12 94469.88 107520.32 108840.35 106601.24] [106868.43 121246.86 117062.7 107363.03 97094. 105566.89 104058.88] 0 [109547.09 95839.12 94469.88 107520.32 108840.35 106601.24 108556.59] [121246.86 117062.7 107363.03 97094. 105566.89 104058.88 109547.09] 0 [ 95839.12 94469.88 107520.32 108840.35 106601.24 108556.59 104357.55] [117062.7 107363.03 97094. 105566.89 104058.88 109547.09 95839.12] 0 [ 94469.88 107520.32 108840.35 106601.24 108556.59 104357.55 88403.9 ] [107363.03 97094. 105566.89 104058.88 109547.09 95839.12 94469.88] 0 [107520.32 108840.35 106601.24 108556.59 104357.55 88403.9 102897.39] [ 97094. 105566.89 104058.88 109547.09 95839.12 94469.88 107520.32] 0 [108840.35 106601.24 108556.59 104357.55 88403.9 102897.39 90430.14] [105566.89 104058.88 109547.09 95839.12 94469.88 107520.32 108840.35] 0 [106601.24 108556.59 104357.55 88403.9 102897.39 90430.14 97417.87] [104058.88 109547.09 95839.12 94469.88 107520.32 108840.35 106601.24] 0 [108556.59 104357.55 88403.9 102897.39 90430.14 97417.87 104826.28] [109547.09 95839.12 94469.88 107520.32 108840.35 106601.24 108556.59] 0 [104357.55 88403.9 102897.39 90430.14 97417.87 104826.28 98594.68] [ 95839.12 94469.88 107520.32 108840.35 106601.24 108556.59 104357.55] 0 [ 88403.9 102897.39 90430.14 97417.87 104826.28 98594.68 107600.06] [ 94469.88 107520.32 108840.35 106601.24 108556.59 104357.55 88403.9 ] 0 [102897.39 90430.14 97417.87 104826.28 98594.68 107600.06 100190.49] [107520.32 108840.35 106601.24 108556.59 104357.55 88403.9 102897.39] 0 [ 90430.14 97417.87 104826.28 98594.68 107600.06 100190.49 99483.46] [108840.35 106601.24 108556.59 104357.55 88403.9 102897.39 90430.14] 0 [ 97417.87 104826.28 98594.68 107600.06 100190.49 99483.46 111463.25] [106601.24 108556.59 104357.55 88403.9 102897.39 90430.14 97417.87] 0 [104826.28 98594.68 107600.06 100190.49 99483.46 111463.25 94999.42] [108556.59 104357.55 88403.9 102897.39 90430.14 97417.87 104826.28] 0 [ 98594.68 107600.06 100190.49 99483.46 111463.25 94999.42 100134.02] [104357.55 88403.9 102897.39 90430.14 97417.87 104826.28 98594.68] 0 [107600.06 100190.49 99483.46 111463.25 94999.42 100134.02 88183.93] [ 88403.9 102897.39 90430.14 97417.87 104826.28 98594.68 107600.06] 0 [100190.49 99483.46 111463.25 94999.42 100134.02 88183.93 112927.14] [102897.39 90430.14 97417.87 104826.28 98594.68 107600.06 100190.49] 0 [ 99483.46 111463.25 94999.42 100134.02 88183.93 112927.14 94530.13] [ 90430.14 97417.87 104826.28 98594.68 107600.06 100190.49 99483.46] 0 [111463.25 94999.42 100134.02 88183.93 112927.14 94530.13 104178.51] [ 97417.87 104826.28 98594.68 107600.06 100190.49 99483.46 111463.25] 0 [ 94999.42 100134.02 88183.93 112927.14 94530.13 104178.51 102602.07] [104826.28 98594.68 107600.06 100190.49 99483.46 111463.25 94999.42] 0 [100134.02 88183.93 112927.14 94530.13 104178.51 102602.07 102472.57] [ 98594.68 107600.06 100190.49 99483.46 111463.25 94999.42 100134.02] 0 [ 88183.93 112927.14 94530.13 104178.51 102602.07 102472.57 83974.88] [107600.06 100190.49 99483.46 111463.25 94999.42 100134.02 88183.93] 0 [112927.14 94530.13 104178.51 102602.07 102472.57 83974.88 89857.44] [100190.49 99483.46 111463.25 94999.42 100134.02 88183.93 112927.14] 0 [ 94530.13 104178.51 102602.07 102472.57 83974.88 89857.44 86578.99] [ 99483.46 111463.25 94999.42 100134.02 88183.93 112927.14 94530.13] 0 [104178.51 102602.07 102472.57 83974.88 89857.44 86578.99 84330.43] [111463.25 94999.42 100134.02 88183.93 112927.14 94530.13 104178.51] 0 [102602.07 102472.57 83974.88 89857.44 86578.99 84330.43 86263.85] [ 94999.42 100134.02 88183.93 112927.14 94530.13 104178.51 102602.07] 0 [102472.57 83974.88 89857.44 86578.99 84330.43 86263.85 87910.42] [100134.02 88183.93 112927.14 94530.13 104178.51 102602.07 102472.57] 0 [83974.88 89857.44 86578.99 84330.43 86263.85 87910.42 86937.68] [ 88183.93 112927.14 94530.13 104178.51 102602.07 102472.57 83974.88] 0 [89857.44 86578.99 84330.43 86263.85 87910.42 86937.68 77280.84] [112927.14 94530.13 104178.51 102602.07 102472.57 83974.88 89857.44] 0 [86578.99 84330.43 86263.85 87910.42 86937.68 77280.84 89710.33] [ 94530.13 104178.51 102602.07 102472.57 83974.88 89857.44 86578.99] 0 [84330.43 86263.85 87910.42 86937.68 77280.84 89710.33 99047.37] [104178.51 102602.07 102472.57 83974.88 89857.44 86578.99 84330.43] 0 [86263.85 87910.42 86937.68 77280.84 89710.33 99047.37 71902.78] [102602.07 102472.57 83974.88 89857.44 86578.99 84330.43 86263.85] 0 [87910.42 86937.68 77280.84 89710.33 99047.37 71902.78 92722.1 ] [102472.57 83974.88 89857.44 86578.99 84330.43 86263.85 87910.42] 0 [86937.68 77280.84 89710.33 99047.37 71902.78 92722.1 85441.03] [83974.88 89857.44 86578.99 84330.43 86263.85 87910.42 86937.68] 0 [77280.84 89710.33 99047.37 71902.78 92722.1 85441.03 81959.64] [89857.44 86578.99 84330.43 86263.85 87910.42 86937.68 77280.84] 0 [89710.33 99047.37 71902.78 92722.1 85441.03 81959.64 92288.07] [86578.99 84330.43 86263.85 87910.42 86937.68 77280.84 89710.33] 0 [99047.37 71902.78 92722.1 85441.03 81959.64 92288.07 90844.13] [84330.43 86263.85 87910.42 86937.68 77280.84 89710.33 99047.37] 0 [71902.78 92722.1 85441.03 81959.64 92288.07 90844.13 76149.23] [86263.85 87910.42 86937.68 77280.84 89710.33 99047.37 71902.78] 0 [92722.1 85441.03 81959.64 92288.07 90844.13 76149.23 80709.92] [87910.42 86937.68 77280.84 89710.33 99047.37 71902.78 92722.1 ] 0 [85441.03 81959.64 92288.07 90844.13 76149.23 80709.92 97624.37] [86937.68 77280.84 89710.33 99047.37 71902.78 92722.1 85441.03] 0 [81959.64 92288.07 90844.13 76149.23 80709.92 97624.37 93876.87] [77280.84 89710.33 99047.37 71902.78 92722.1 85441.03 81959.64] 0 [92288.07 90844.13 76149.23 80709.92 97624.37 93876.87 87940.01] [89710.33 99047.37 71902.78 92722.1 85441.03 81959.64 92288.07] 0 [90844.13 76149.23 80709.92 97624.37 93876.87 87940.01 85248.98] [99047.37 71902.78 92722.1 85441.03 81959.64 92288.07 90844.13] 0 [76149.23 80709.92 97624.37 93876.87 87940.01 85248.98 74182.06] [71902.78 92722.1 85441.03 81959.64 92288.07 90844.13 76149.23] 0 [80709.92 97624.37 93876.87 87940.01 85248.98 74182.06 89481.59] [92722.1 85441.03 81959.64 92288.07 90844.13 76149.23 80709.92] 0 [97624.37 93876.87 87940.01 85248.98 74182.06 89481.59 65712.09] [85441.03 81959.64 92288.07 90844.13 76149.23 80709.92 97624.37] 0 [93876.87 87940.01 85248.98 74182.06 89481.59 65712.09 93696.98] [81959.64 92288.07 90844.13 76149.23 80709.92 97624.37 93876.87] 0 [87940.01 85248.98 74182.06 89481.59 65712.09 93696.98 68210.73] [92288.07 90844.13 76149.23 80709.92 97624.37 93876.87 87940.01] 0 [85248.98 74182.06 89481.59 65712.09 93696.98 68210.73 71071.58] [90844.13 76149.23 80709.92 97624.37 93876.87 87940.01 85248.98] 0 [74182.06 89481.59 65712.09 93696.98 68210.73 71071.58 99108.67] [76149.23 80709.92 97624.37 93876.87 87940.01 85248.98 74182.06] 0 [89481.59 65712.09 93696.98 68210.73 71071.58 99108.67 92886.08] [80709.92 97624.37 93876.87 87940.01 85248.98 74182.06 89481.59] 0 [65712.09 93696.98 68210.73 71071.58 99108.67 92886.08 83051.35] [97624.37 93876.87 87940.01 85248.98 74182.06 89481.59 65712.09] 0 [93696.98 68210.73 71071.58 99108.67 92886.08 83051.35 88977.68] [93876.87 87940.01 85248.98 74182.06 89481.59 65712.09 93696.98] 0 [68210.73 71071.58 99108.67 92886.08 83051.35 88977.68 75515.29] [87940.01 85248.98 74182.06 89481.59 65712.09 93696.98 68210.73] 0 [71071.58 99108.67 92886.08 83051.35 88977.68 75515.29 93290.1 ] [85248.98 74182.06 89481.59 65712.09 93696.98 68210.73 71071.58] 0 [ 99108.67 92886.08 83051.35 88977.68 75515.29 93290.1 101568.57] [74182.06 89481.59 65712.09 93696.98 68210.73 71071.58 99108.67] 0 [ 92886.08 83051.35 88977.68 75515.29 93290.1 101568.57 71913.59] [89481.59 65712.09 93696.98 68210.73 71071.58 99108.67 92886.08] 0 [ 83051.35 88977.68 75515.29 93290.1 101568.57 71913.59 99192.27] [65712.09 93696.98 68210.73 71071.58 99108.67 92886.08 83051.35] 0 [ 88977.68 75515.29 93290.1 101568.57 71913.59 99192.27 70016.49] [93696.98 68210.73 71071.58 99108.67 92886.08 83051.35 88977.68] 0 [ 75515.29 93290.1 101568.57 71913.59 99192.27 70016.49 83828.55] [68210.73 71071.58 99108.67 92886.08 83051.35 88977.68 75515.29] 0 [ 93290.1 101568.57 71913.59 99192.27 70016.49 83828.55 83197.8 ] [71071.58 99108.67 92886.08 83051.35 88977.68 75515.29 93290.1 ] 0 [101568.57 71913.59 99192.27 70016.49 83828.55 83197.8 88797.55] [ 99108.67 92886.08 83051.35 88977.68 75515.29 93290.1 101568.57] 0 [71913.59 99192.27 70016.49 83828.55 83197.8 88797.55 80060.81] [ 92886.08 83051.35 88977.68 75515.29 93290.1 101568.57 71913.59] 0 [99192.27 70016.49 83828.55 83197.8 88797.55 80060.81 78046.82] [ 83051.35 88977.68 75515.29 93290.1 101568.57 71913.59 99192.27] 0 [70016.49 83828.55 83197.8 88797.55 80060.81 78046.82 97790.29] [ 88977.68 75515.29 93290.1 101568.57 71913.59 99192.27 70016.49] 0 [83828.55 83197.8 88797.55 80060.81 78046.82 97790.29 94327.87] [ 75515.29 93290.1 101568.57 71913.59 99192.27 70016.49 83828.55] 0 [83197.8 88797.55 80060.81 78046.82 97790.29 94327.87 89724.32] [ 93290.1 101568.57 71913.59 99192.27 70016.49 83828.55 83197.8 ] 0 [88797.55 80060.81 78046.82 97790.29 94327.87 89724.32 87528.91] [101568.57 71913.59 99192.27 70016.49 83828.55 83197.8 88797.55] 0 [80060.81 78046.82 97790.29 94327.87 89724.32 87528.91 78491.1 ] [71913.59 99192.27 70016.49 83828.55 83197.8 88797.55 80060.81] 0 [78046.82 97790.29 94327.87 89724.32 87528.91 78491.1 96064.96] [99192.27 70016.49 83828.55 83197.8 88797.55 80060.81 78046.82] 0 [97790.29 94327.87 89724.32 87528.91 78491.1 96064.96 73422.18] [70016.49 83828.55 83197.8 88797.55 80060.81 78046.82 97790.29] 0 [94327.87 89724.32 87528.91 78491.1 96064.96 73422.18 83558.35] [83828.55 83197.8 88797.55 80060.81 78046.82 97790.29 94327.87] 0 [ 89724.32 87528.91 78491.1 96064.96 73422.18 83558.35 103158.63] [83197.8 88797.55 80060.81 78046.82 97790.29 94327.87 89724.32] 0 [ 87528.91 78491.1 96064.96 73422.18 83558.35 103158.63 89764.94] [88797.55 80060.81 78046.82 97790.29 94327.87 89724.32 87528.91] 0 [ 78491.1 96064.96 73422.18 83558.35 103158.63 89764.94 74702.27] [80060.81 78046.82 97790.29 94327.87 89724.32 87528.91 78491.1 ] 0 [ 96064.96 73422.18 83558.35 103158.63 89764.94 74702.27 83386.68] [78046.82 97790.29 94327.87 89724.32 87528.91 78491.1 96064.96] 0 [ 73422.18 83558.35 103158.63 89764.94 74702.27 83386.68 73964.09] [97790.29 94327.87 89724.32 87528.91 78491.1 96064.96 73422.18] 0 [ 83558.35 103158.63 89764.94 74702.27 83386.68 73964.09 71186.93] [94327.87 89724.32 87528.91 78491.1 96064.96 73422.18 83558.35] 0 [103158.63 89764.94 74702.27 83386.68 73964.09 71186.93 80307.2 ] [ 89724.32 87528.91 78491.1 96064.96 73422.18 83558.35 103158.63] 0 [89764.94 74702.27 83386.68 73964.09 71186.93 80307.2 86577.33] [ 87528.91 78491.1 96064.96 73422.18 83558.35 103158.63 89764.94] 0 [74702.27 83386.68 73964.09 71186.93 80307.2 86577.33 76997.7 ] [ 78491.1 96064.96 73422.18 83558.35 103158.63 89764.94 74702.27] 0 [83386.68 73964.09 71186.93 80307.2 86577.33 76997.7 87125.34] [ 96064.96 73422.18 83558.35 103158.63 89764.94 74702.27 83386.68] 0 [73964.09 71186.93 80307.2 86577.33 76997.7 87125.34 90054.95] [ 73422.18 83558.35 103158.63 89764.94 74702.27 83386.68 73964.09] 0 [71186.93 80307.2 86577.33 76997.7 87125.34 90054.95 71906.51] [ 83558.35 103158.63 89764.94 74702.27 83386.68 73964.09 71186.93] 0 [80307.2 86577.33 76997.7 87125.34 90054.95 71906.51 68826.18] [103158.63 89764.94 74702.27 83386.68 73964.09 71186.93 80307.2 ] 0 [86577.33 76997.7 87125.34 90054.95 71906.51 68826.18 75548.63] [89764.94 74702.27 83386.68 73964.09 71186.93 80307.2 86577.33] 0 [76997.7 87125.34 90054.95 71906.51 68826.18 75548.63 70103.42] [74702.27 83386.68 73964.09 71186.93 80307.2 86577.33 76997.7 ] 0 [87125.34 90054.95 71906.51 68826.18 75548.63 70103.42 76218.91] [83386.68 73964.09 71186.93 80307.2 86577.33 76997.7 87125.34] 0 [90054.95 71906.51 68826.18 75548.63 70103.42 76218.91 82986.52] [73964.09 71186.93 80307.2 86577.33 76997.7 87125.34 90054.95] 0 [71906.51 68826.18 75548.63 70103.42 76218.91 82986.52 74187.7 ] [71186.93 80307.2 86577.33 76997.7 87125.34 90054.95 71906.51] 0 [68826.18 75548.63 70103.42 76218.91 82986.52 74187.7 64654.7 ] [80307.2 86577.33 76997.7 87125.34 90054.95 71906.51 68826.18] 0 [75548.63 70103.42 76218.91 82986.52 74187.7 64654.7 78307.34] [86577.33 76997.7 87125.34 90054.95 71906.51 68826.18 75548.63] 0 [70103.42 76218.91 82986.52 74187.7 64654.7 78307.34 94355.45] [76997.7 87125.34 90054.95 71906.51 68826.18 75548.63 70103.42] 0 [76218.91 82986.52 74187.7 64654.7 78307.34 94355.45 77036.21] [87125.34 90054.95 71906.51 68826.18 75548.63 70103.42 76218.91] 0 [82986.52 74187.7 64654.7 78307.34 94355.45 77036.21 66382.15] [90054.95 71906.51 68826.18 75548.63 70103.42 76218.91 82986.52] 0 [74187.7 64654.7 78307.34 94355.45 77036.21 66382.15 69096.71] [71906.51 68826.18 75548.63 70103.42 76218.91 82986.52 74187.7 ] 0 [64654.7 78307.34 94355.45 77036.21 66382.15 69096.71 83469.05] [68826.18 75548.63 70103.42 76218.91 82986.52 74187.7 64654.7 ] 0 [78307.34 94355.45 77036.21 66382.15 69096.71 83469.05 73289.86] [75548.63 70103.42 76218.91 82986.52 74187.7 64654.7 78307.34] 0 [94355.45 77036.21 66382.15 69096.71 83469.05 73289.86 66035.39] [70103.42 76218.91 82986.52 74187.7 64654.7 78307.34 94355.45] 0 [77036.21 66382.15 69096.71 83469.05 73289.86 66035.39 71856.51] [76218.91 82986.52 74187.7 64654.7 78307.34 94355.45 77036.21] 0 [66382.15 69096.71 83469.05 73289.86 66035.39 71856.51 59680.02] [82986.52 74187.7 64654.7 78307.34 94355.45 77036.21 66382.15] 0 [69096.71 83469.05 73289.86 66035.39 71856.51 59680.02 72935.79] [74187.7 64654.7 78307.34 94355.45 77036.21 66382.15 69096.71] 0 [83469.05 73289.86 66035.39 71856.51 59680.02 72935.79 74568.61] [64654.7 78307.34 94355.45 77036.21 66382.15 69096.71 83469.05] 0 [73289.86 66035.39 71856.51 59680.02 72935.79 74568.61 52757.4 ] [78307.34 94355.45 77036.21 66382.15 69096.71 83469.05 73289.86] 0 [66035.39 71856.51 59680.02 72935.79 74568.61 52757.4 80263.09] [94355.45 77036.21 66382.15 69096.71 83469.05 73289.86 66035.39] 0 [71856.51 59680.02 72935.79 74568.61 52757.4 80263.09 60101.36] [77036.21 66382.15 69096.71 83469.05 73289.86 66035.39 71856.51] 0 [59680.02 72935.79 74568.61 52757.4 80263.09 60101.36 66096.58] [66382.15 69096.71 83469.05 73289.86 66035.39 71856.51 59680.02] 0 [72935.79 74568.61 52757.4 80263.09 60101.36 66096.58 80290.08] [69096.71 83469.05 73289.86 66035.39 71856.51 59680.02 72935.79] 0 [74568.61 52757.4 80263.09 60101.36 66096.58 80290.08 73097. ] [83469.05 73289.86 66035.39 71856.51 59680.02 72935.79 74568.61] 0 [52757.4 80263.09 60101.36 66096.58 80290.08 73097. 68397.78] [73289.86 66035.39 71856.51 59680.02 72935.79 74568.61 52757.4 ] 0 [80263.09 60101.36 66096.58 80290.08 73097. 68397.78 74232.34] [66035.39 71856.51 59680.02 72935.79 74568.61 52757.4 80263.09] 0 [60101.36 66096.58 80290.08 73097. 68397.78 74232.34 72832.66] [71856.51 59680.02 72935.79 74568.61 52757.4 80263.09 60101.36] 0 [66096.58 80290.08 73097. 68397.78 74232.34 72832.66 65537.87] [59680.02 72935.79 74568.61 52757.4 80263.09 60101.36 66096.58] 0 [80290.08 73097. 68397.78 74232.34 72832.66 65537.87 65293.6 ] [72935.79 74568.61 52757.4 80263.09 60101.36 66096.58 80290.08] 0 [73097. 68397.78 74232.34 72832.66 65537.87 65293.6 67517.02] [74568.61 52757.4 80263.09 60101.36 66096.58 80290.08 73097. ] 0 [68397.78 74232.34 72832.66 65537.87 65293.6 67517.02 72007.98] [52757.4 80263.09 60101.36 66096.58 80290.08 73097. 68397.78] 0 [74232.34 72832.66 65537.87 65293.6 67517.02 72007.98 61729.96] [80263.09 60101.36 66096.58 80290.08 73097. 68397.78 74232.34] 0 [72832.66 65537.87 65293.6 67517.02 72007.98 61729.96 71465.8 ] [60101.36 66096.58 80290.08 73097. 68397.78 74232.34 72832.66] 0 [65537.87 65293.6 67517.02 72007.98 61729.96 71465.8 67354.8 ] [66096.58 80290.08 73097. 68397.78 74232.34 72832.66 65537.87] 0 [65293.6 67517.02 72007.98 61729.96 71465.8 67354.8 69795.76] [80290.08 73097. 68397.78 74232.34 72832.66 65537.87 65293.6 ] 0 [67517.02 72007.98 61729.96 71465.8 67354.8 69795.76 70100.62] [73097. 68397.78 74232.34 72832.66 65537.87 65293.6 67517.02] 0 [72007.98 61729.96 71465.8 67354.8 69795.76 70100.62 63818.45] [68397.78 74232.34 72832.66 65537.87 65293.6 67517.02 72007.98] 0 [61729.96 71465.8 67354.8 69795.76 70100.62 63818.45 79204.76] [74232.34 72832.66 65537.87 65293.6 67517.02 72007.98 61729.96] 0 [71465.8 67354.8 69795.76 70100.62 63818.45 79204.76 73991.24] [72832.66 65537.87 65293.6 67517.02 72007.98 61729.96 71465.8 ] 0 [67354.8 69795.76 70100.62 63818.45 79204.76 73991.24 77417.94] [65537.87 65293.6 67517.02 72007.98 61729.96 71465.8 67354.8 ] 0 [69795.76 70100.62 63818.45 79204.76 73991.24 77417.94 73734.03] [65293.6 67517.02 72007.98 61729.96 71465.8 67354.8 69795.76] 0 [70100.62 63818.45 79204.76 73991.24 77417.94 73734.03 78021.81] [67517.02 72007.98 61729.96 71465.8 67354.8 69795.76 70100.62] 0 [63818.45 79204.76 73991.24 77417.94 73734.03 78021.81 79128.94] [72007.98 61729.96 71465.8 67354.8 69795.76 70100.62 63818.45] 0 [79204.76 73991.24 77417.94 73734.03 78021.81 79128.94 72458.42] [61729.96 71465.8 67354.8 69795.76 70100.62 63818.45 79204.76] 0 [73991.24 77417.94 73734.03 78021.81 79128.94 72458.42 57509.07] [71465.8 67354.8 69795.76 70100.62 63818.45 79204.76 73991.24] 0 [77417.94 73734.03 78021.81 79128.94 72458.42 57509.07 61701.17] [67354.8 69795.76 70100.62 63818.45 79204.76 73991.24 77417.94] 0 [73734.03 78021.81 79128.94 72458.42 57509.07 61701.17 76248.04] [69795.76 70100.62 63818.45 79204.76 73991.24 77417.94 73734.03] 0 [78021.81 79128.94 72458.42 57509.07 61701.17 76248.04 63457.36] [70100.62 63818.45 79204.76 73991.24 77417.94 73734.03 78021.81] 0 [79128.94 72458.42 57509.07 61701.17 76248.04 63457.36 62748.11] [63818.45 79204.76 73991.24 77417.94 73734.03 78021.81 79128.94] 0 [72458.42 57509.07 61701.17 76248.04 63457.36 62748.11 62499.47] [79204.76 73991.24 77417.94 73734.03 78021.81 79128.94 72458.42] 0 [57509.07 61701.17 76248.04 63457.36 62748.11 62499.47 47650.21] [73991.24 77417.94 73734.03 78021.81 79128.94 72458.42 57509.07] 0 [61701.17 76248.04 63457.36 62748.11 62499.47 47650.21 69703.87] [77417.94 73734.03 78021.81 79128.94 72458.42 57509.07 61701.17] 0 [76248.04 63457.36 62748.11 62499.47 47650.21 69703.87 66520.31] [73734.03 78021.81 79128.94 72458.42 57509.07 61701.17 76248.04] 0 [63457.36 62748.11 62499.47 47650.21 69703.87 66520.31 77652.56] [78021.81 79128.94 72458.42 57509.07 61701.17 76248.04 63457.36] 0 [62748.11 62499.47 47650.21 69703.87 66520.31 77652.56 79076.33] [79128.94 72458.42 57509.07 61701.17 76248.04 63457.36 62748.11] 0 [62499.47 47650.21 69703.87 66520.31 77652.56 79076.33 65855.62] [72458.42 57509.07 61701.17 76248.04 63457.36 62748.11 62499.47] 0 [47650.21 69703.87 66520.31 77652.56 79076.33 65855.62 71712.99] [57509.07 61701.17 76248.04 63457.36 62748.11 62499.47 47650.21] 0 [69703.87 66520.31 77652.56 79076.33 65855.62 71712.99 72914.96] [61701.17 76248.04 63457.36 62748.11 62499.47 47650.21 69703.87] 0 [66520.31 77652.56 79076.33 65855.62 71712.99 72914.96 71994.49] [76248.04 63457.36 62748.11 62499.47 47650.21 69703.87 66520.31] 0 [ 77652.56 79076.33 65855.62 71712.99 72914.96 71994.49 132786.6 ] [63457.36 62748.11 62499.47 47650.21 69703.87 66520.31 77652.56] 0 [ 79076.33 65855.62 71712.99 72914.96 71994.49 132786.6 113327.73] [62748.11 62499.47 47650.21 69703.87 66520.31 77652.56 79076.33] 0 [ 65855.62 71712.99 72914.96 71994.49 132786.6 113327.73 99531.58] [62499.47 47650.21 69703.87 66520.31 77652.56 79076.33 65855.62] 0 [ 71712.99 72914.96 71994.49 132786.6 113327.73 99531.58 120463.58] [47650.21 69703.87 66520.31 77652.56 79076.33 65855.62 71712.99] 0 [ 72914.96 71994.49 132786.6 113327.73 99531.58 120463.58 121922.33] [69703.87 66520.31 77652.56 79076.33 65855.62 71712.99 72914.96] 0 [ 71994.49 132786.6 113327.73 99531.58 120463.58 121922.33 134115.37] [66520.31 77652.56 79076.33 65855.62 71712.99 72914.96 71994.49] 0 [132786.6 113327.73 99531.58 120463.58 121922.33 134115.37 121190.53] [ 77652.56 79076.33 65855.62 71712.99 72914.96 71994.49 132786.6 ] 0 [113327.73 99531.58 120463.58 121922.33 134115.37 121190.53 107964.23] [ 79076.33 65855.62 71712.99 72914.96 71994.49 132786.6 113327.73] 0 [ 99531.58 120463.58 121922.33 134115.37 121190.53 107964.23 105497.73] [ 65855.62 71712.99 72914.96 71994.49 132786.6 113327.73 99531.58] 0 [120463.58 121922.33 134115.37 121190.53 107964.23 105497.73 124636.28] [ 71712.99 72914.96 71994.49 132786.6 113327.73 99531.58 120463.58] 0 [121922.33 134115.37 121190.53 107964.23 105497.73 124636.28 116284.94] [ 72914.96 71994.49 132786.6 113327.73 99531.58 120463.58 121922.33] 0 [134115.37 121190.53 107964.23 105497.73 124636.28 116284.94 124053.35] [ 71994.49 132786.6 113327.73 99531.58 120463.58 121922.33 134115.37] 0 [121190.53 107964.23 105497.73 124636.28 116284.94 124053.35 121741.02] [132786.6 113327.73 99531.58 120463.58 121922.33 134115.37 121190.53] 0 [107964.23 105497.73 124636.28 116284.94 124053.35 121741.02 127715.4 ] [113327.73 99531.58 120463.58 121922.33 134115.37 121190.53 107964.23] 0 [105497.73 124636.28 116284.94 124053.35 121741.02 127715.4 107102.62] [ 99531.58 120463.58 121922.33 134115.37 121190.53 107964.23 105497.73] 0 [124636.28 116284.94 124053.35 121741.02 127715.4 107102.62 113259.78] [120463.58 121922.33 134115.37 121190.53 107964.23 105497.73 124636.28] 0 [116284.94 124053.35 121741.02 127715.4 107102.62 113259.78 129786.97] [121922.33 134115.37 121190.53 107964.23 105497.73 124636.28 116284.94] 0 [124053.35 121741.02 127715.4 107102.62 113259.78 129786.97 118992.44] [134115.37 121190.53 107964.23 105497.73 124636.28 116284.94 124053.35] 0 [121741.02 127715.4 107102.62 113259.78 129786.97 118992.44 116827.12] [121190.53 107964.23 105497.73 124636.28 116284.94 124053.35 121741.02] 0 [127715.4 107102.62 113259.78 129786.97 118992.44 116827.12 128998.7 ] [107964.23 105497.73 124636.28 116284.94 124053.35 121741.02 127715.4 ] 0 [107102.62 113259.78 129786.97 118992.44 116827.12 128998.7 113843.47] [105497.73 124636.28 116284.94 124053.35 121741.02 127715.4 107102.62] 0 [113259.78 129786.97 118992.44 116827.12 128998.7 113843.47 133720.76] [124636.28 116284.94 124053.35 121741.02 127715.4 107102.62 113259.78] 0 [129786.97 118992.44 116827.12 128998.7 113843.47 133720.76 117459.51] [116284.94 124053.35 121741.02 127715.4 107102.62 113259.78 129786.97] 0 [118992.44 116827.12 128998.7 113843.47 133720.76 117459.51 109379.05] [124053.35 121741.02 127715.4 107102.62 113259.78 129786.97 118992.44] 0 [116827.12 128998.7 113843.47 133720.76 117459.51 109379.05 123340.75] [121741.02 127715.4 107102.62 113259.78 129786.97 118992.44 116827.12] 0 [128998.7 113843.47 133720.76 117459.51 109379.05 123340.75 126110.97] [127715.4 107102.62 113259.78 129786.97 118992.44 116827.12 128998.7 ] 0 [113843.47 133720.76 117459.51 109379.05 123340.75 126110.97 119210.57] [107102.62 113259.78 129786.97 118992.44 116827.12 128998.7 113843.47] 0 [133720.76 117459.51 109379.05 123340.75 126110.97 119210.57 125558.1 ] [113259.78 129786.97 118992.44 116827.12 128998.7 113843.47 133720.76] 0 [117459.51 109379.05 123340.75 126110.97 119210.57 125558.1 132823.51] [129786.97 118992.44 116827.12 128998.7 113843.47 133720.76 117459.51] 0 [109379.05 123340.75 126110.97 119210.57 125558.1 132823.51 98472.98] [118992.44 116827.12 128998.7 113843.47 133720.76 117459.51 109379.05] 0 [123340.75 126110.97 119210.57 125558.1 132823.51 98472.98 129436.86] [116827.12 128998.7 113843.47 133720.76 117459.51 109379.05 123340.75] 0 [126110.97 119210.57 125558.1 132823.51 98472.98 129436.86 106157.8 ] [128998.7 113843.47 133720.76 117459.51 109379.05 123340.75 126110.97] 0 [119210.57 125558.1 132823.51 98472.98 129436.86 106157.8 125137.4 ] [113843.47 133720.76 117459.51 109379.05 123340.75 126110.97 119210.57] 0 [125558.1 132823.51 98472.98 129436.86 106157.8 125137.4 89416.58] [133720.76 117459.51 109379.05 123340.75 126110.97 119210.57 125558.1 ] 0 [132823.51 98472.98 129436.86 106157.8 125137.4 89416.58 103241.81] [117459.51 109379.05 123340.75 126110.97 119210.57 125558.1 132823.51] 0 [ 98472.98 129436.86 106157.8 125137.4 89416.58 103241.81 105327.41] [109379.05 123340.75 126110.97 119210.57 125558.1 132823.51 98472.98] 0 [129436.86 106157.8 125137.4 89416.58 103241.81 105327.41 93303.17] [123340.75 126110.97 119210.57 125558.1 132823.51 98472.98 129436.86] 0 [106157.8 125137.4 89416.58 103241.81 105327.41 93303.17 117206.52] [126110.97 119210.57 125558.1 132823.51 98472.98 129436.86 106157.8 ] 0 [125137.4 89416.58 103241.81 105327.41 93303.17 117206.52 104630.69] [119210.57 125558.1 132823.51 98472.98 129436.86 106157.8 125137.4 ] 0 [ 89416.58 103241.81 105327.41 93303.17 117206.52 104630.69 114811.41] [125558.1 132823.51 98472.98 129436.86 106157.8 125137.4 89416.58] 0 [103241.81 105327.41 93303.17 117206.52 104630.69 114811.41 104460.64] [132823.51 98472.98 129436.86 106157.8 125137.4 89416.58 103241.81] 0 [105327.41 93303.17 117206.52 104630.69 114811.41 104460.64 110245.73] [ 98472.98 129436.86 106157.8 125137.4 89416.58 103241.81 105327.41] 0 [ 93303.17 117206.52 104630.69 114811.41 104460.64 110245.73 99896.84] [129436.86 106157.8 125137.4 89416.58 103241.81 105327.41 93303.17] 0 [117206.52 104630.69 114811.41 104460.64 110245.73 99896.84 93110.99] [106157.8 125137.4 89416.58 103241.81 105327.41 93303.17 117206.52] 0 [104630.69 114811.41 104460.64 110245.73 99896.84 93110.99 99014.18] [125137.4 89416.58 103241.81 105327.41 93303.17 117206.52 104630.69] 0 [114811.41 104460.64 110245.73 99896.84 93110.99 99014.18 109963.5 ] [ 89416.58 103241.81 105327.41 93303.17 117206.52 104630.69 114811.41] 0 [104460.64 110245.73 99896.84 93110.99 99014.18 109963.5 93456.07] [103241.81 105327.41 93303.17 117206.52 104630.69 114811.41 104460.64] 0 [110245.73 99896.84 93110.99 99014.18 109963.5 93456.07 103556.85] [105327.41 93303.17 117206.52 104630.69 114811.41 104460.64 110245.73] 0 [ 99896.84 93110.99 99014.18 109963.5 93456.07 103556.85 99134.37] [ 93303.17 117206.52 104630.69 114811.41 104460.64 110245.73 99896.84] 0 [ 93110.99 99014.18 109963.5 93456.07 103556.85 99134.37 110737.98] [117206.52 104630.69 114811.41 104460.64 110245.73 99896.84 93110.99] 0 [ 99014.18 109963.5 93456.07 103556.85 99134.37 110737.98 116453.47] [104630.69 114811.41 104460.64 110245.73 99896.84 93110.99 99014.18] 0 [109963.5 93456.07 103556.85 99134.37 110737.98 116453.47 98163.94] [114811.41 104460.64 110245.73 99896.84 93110.99 99014.18 109963.5 ] 0 [ 93456.07 103556.85 99134.37 110737.98 116453.47 98163.94 115242.35] [104460.64 110245.73 99896.84 93110.99 99014.18 109963.5 93456.07] 0 [103556.85 99134.37 110737.98 116453.47 98163.94 115242.35 102251.5 ] [110245.73 99896.84 93110.99 99014.18 109963.5 93456.07 103556.85] 0 [ 99134.37 110737.98 116453.47 98163.94 115242.35 102251.5 129545.49] [ 99896.84 93110.99 99014.18 109963.5 93456.07 103556.85 99134.37] 0 [110737.98 116453.47 98163.94 115242.35 102251.5 129545.49 120416.75] [ 93110.99 99014.18 109963.5 93456.07 103556.85 99134.37 110737.98] 0 [116453.47 98163.94 115242.35 102251.5 129545.49 120416.75 95126.05] [ 99014.18 109963.5 93456.07 103556.85 99134.37 110737.98 116453.47] 0 [ 98163.94 115242.35 102251.5 129545.49 120416.75 95126.05 127428.82] [109963.5 93456.07 103556.85 99134.37 110737.98 116453.47 98163.94] 0 [115242.35 102251.5 129545.49 120416.75 95126.05 127428.82 95565.81] [ 93456.07 103556.85 99134.37 110737.98 116453.47 98163.94 115242.35] 0 [102251.5 129545.49 120416.75 95126.05 127428.82 95565.81 91509.29] [103556.85 99134.37 110737.98 116453.47 98163.94 115242.35 102251.5 ] 0 [129545.49 120416.75 95126.05 127428.82 95565.81 91509.29 106087.27] [ 99134.37 110737.98 116453.47 98163.94 115242.35 102251.5 129545.49] 0 [120416.75 95126.05 127428.82 95565.81 91509.29 106087.27 147496.75] [110737.98 116453.47 98163.94 115242.35 102251.5 129545.49 120416.75] 0 [ 95126.05 127428.82 95565.81 91509.29 106087.27 147496.75 148318.37] [116453.47 98163.94 115242.35 102251.5 129545.49 120416.75 95126.05] 0 [127428.82 95565.81 91509.29 106087.27 147496.75 148318.37 142006.18] [ 98163.94 115242.35 102251.5 129545.49 120416.75 95126.05 127428.82] 0 [ 95565.81 91509.29 106087.27 147496.75 148318.37 142006.18 165977.89] [115242.35 102251.5 129545.49 120416.75 95126.05 127428.82 95565.81] 0 [ 91509.29 106087.27 147496.75 148318.37 142006.18 165977.89 153811.15] [102251.5 129545.49 120416.75 95126.05 127428.82 95565.81 91509.29] 0 [106087.27 147496.75 148318.37 142006.18 165977.89 153811.15 151185.28] [129545.49 120416.75 95126.05 127428.82 95565.81 91509.29 106087.27] 0 [147496.75 148318.37 142006.18 165977.89 153811.15 151185.28 139109.31] [120416.75 95126.05 127428.82 95565.81 91509.29 106087.27 147496.75] 0 [148318.37 142006.18 165977.89 153811.15 151185.28 139109.31 142497. ] [ 95126.05 127428.82 95565.81 91509.29 106087.27 147496.75 148318.37] 0 [142006.18 165977.89 153811.15 151185.28 139109.31 142497. 146717.44] [127428.82 95565.81 91509.29 106087.27 147496.75 148318.37 142006.18] 0 [165977.89 153811.15 151185.28 139109.31 142497. 146717.44 162234.28] [ 95565.81 91509.29 106087.27 147496.75 148318.37 142006.18 165977.89] 0 [153811.15 151185.28 139109.31 142497. 146717.44 162234.28 145508.87] [ 91509.29 106087.27 147496.75 148318.37 142006.18 165977.89 153811.15] 0 [151185.28 139109.31 142497. 146717.44 162234.28 145508.87 143294.24] [106087.27 147496.75 148318.37 142006.18 165977.89 153811.15 151185.28] 0 [139109.31 142497. 146717.44 162234.28 145508.87 143294.24 152702. ] [147496.75 148318.37 142006.18 165977.89 153811.15 151185.28 139109.31] 0 [142497. 146717.44 162234.28 145508.87 143294.24 152702. 141190.88] [148318.37 142006.18 165977.89 153811.15 151185.28 139109.31 142497. ] 0 [146717.44 162234.28 145508.87 143294.24 152702. 141190.88 142537.8 ] [142006.18 165977.89 153811.15 151185.28 139109.31 142497. 146717.44] 0 [162234.28 145508.87 143294.24 152702. 141190.88 142537.8 162437.41] [165977.89 153811.15 151185.28 139109.31 142497. 146717.44 162234.28] 0 [145508.87 143294.24 152702. 141190.88 142537.8 162437.41 156335.63] [153811.15 151185.28 139109.31 142497. 146717.44 162234.28 145508.87] 0 [143294.24 152702. 141190.88 142537.8 162437.41 156335.63 153615.38] [151185.28 139109.31 142497. 146717.44 162234.28 145508.87 143294.24] 0 [152702. 141190.88 142537.8 162437.41 156335.63 153615.38 136908.24] [139109.31 142497. 146717.44 162234.28 145508.87 143294.24 152702. ] 0 [141190.88 142537.8 162437.41 156335.63 153615.38 136908.24 159641.83] [142497. 146717.44 162234.28 145508.87 143294.24 152702. 141190.88] 0 [142537.8 162437.41 156335.63 153615.38 136908.24 159641.83 154254.87] [146717.44 162234.28 145508.87 143294.24 152702. 141190.88 142537.8 ] 0 [162437.41 156335.63 153615.38 136908.24 159641.83 154254.87 146827.73] [162234.28 145508.87 143294.24 152702. 141190.88 142537.8 162437.41] 0 [156335.63 153615.38 136908.24 159641.83 154254.87 146827.73 151015.66] [145508.87 143294.24 152702. 141190.88 142537.8 162437.41 156335.63] 0 [153615.38 136908.24 159641.83 154254.87 146827.73 151015.66 152211.89] [143294.24 152702. 141190.88 142537.8 162437.41 156335.63 153615.38] 0 [136908.24 159641.83 154254.87 146827.73 151015.66 152211.89 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151493.15 130329.86 8503.4 ]
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array([136908.24, 159641.83, 154254.87, 146827.73, 151015.66, 152211.89,
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y
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array([54944.27, 56114.35, 78304.82, 47792.25, 61021.67, 49844.91,
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array([63988.26, 51065.39, 54931.56, 48291.98, 56709.41, 67913.55,
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143976.79]),
array([154254.87, 146827.73, 151015.66, 152211.89, 151431.98, 143976.79,
125957.79]),
array([146827.73, 151015.66, 152211.89, 151431.98, 143976.79, 125957.79,
133230.34]),
array([151015.66, 152211.89, 151431.98, 143976.79, 125957.79, 133230.34,
154159.42]),
array([152211.89, 151431.98, 143976.79, 125957.79, 133230.34, 154159.42,
151493.15]),
array([151431.98, 143976.79, 125957.79, 133230.34, 154159.42, 151493.15,
130329.86]),
array([143976.79, 125957.79, 133230.34, 154159.42, 151493.15, 130329.86,
8503.4 ])]
x = np.array(x)
y = np.array(y)
y.shape
(353, 7)
x.shape
(353, 7)
x = np.expand_dims(x,-1)
_ ,X_test ,_1 ,y_test = train_test_split(x ,y ,shuffle=False)
X_train ,X_cv ,y_train ,y_cv = train_test_split(_ ,_1 ,shuffle=False)
X_train10 = (X_train)/m
y_train10 = (y_train)/m
X_cv10 = (X_cv)/m
y_cv10 = (y_cv)/m
X_test10 = (X_test)/m
y_test10 = (y_test)/m
import tensorflow
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense ,Bidirectional ,LSTM ,Lambda ,Input ,BatchNormalization
from tensorflow.keras.losses import mean_squared_error
from tensorflow.keras.optimizers import Adam
lstm_model = Sequential([
Input(X_train.shape[1:]),
Bidirectional(LSTM(units=7*6,return_sequences=True)),
Bidirectional(LSTM(units=7*5)),
Dense(units=7*4, activation='swish'),
Lambda(lambda x : x+0.01),
Dense(units=7*3, activation='relu'),
Dense(units=7*2, activation='relu'),
Dense(units=7, activation='linear')
])
lstm_model.compile(loss='mse' ,optimizer=Adam(learning_rate=0.0001) ,metrics='mse')
history = lstm_model.fit(x = X_train10 ,y=y_train10 ,validation_data=(X_cv10 ,y_cv10) ,epochs=1000)
Epoch 1/1000 7/7 [==============================] - 38s 859ms/step - loss: 0.4428 - mse: 0.4428 - val_loss: 0.2873 - val_mse: 0.2873 Epoch 2/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.4342 - mse: 0.4342 - val_loss: 0.2815 - val_mse: 0.2815 Epoch 3/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.4265 - mse: 0.4265 - val_loss: 0.2761 - val_mse: 0.2761 Epoch 4/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.4193 - mse: 0.4193 - val_loss: 0.2710 - val_mse: 0.2710 Epoch 5/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.4124 - mse: 0.4124 - val_loss: 0.2659 - val_mse: 0.2659 Epoch 6/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.4052 - mse: 0.4052 - val_loss: 0.2604 - val_mse: 0.2604 Epoch 7/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.3972 - mse: 0.3972 - val_loss: 0.2544 - val_mse: 0.2544 Epoch 8/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.3884 - mse: 0.3884 - val_loss: 0.2477 - val_mse: 0.2477 Epoch 9/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.3786 - mse: 0.3786 - val_loss: 0.2402 - val_mse: 0.2402 Epoch 10/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.3674 - mse: 0.3674 - val_loss: 0.2320 - val_mse: 0.2320 Epoch 11/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.3550 - mse: 0.3550 - val_loss: 0.2227 - val_mse: 0.2227 Epoch 12/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.3410 - mse: 0.3410 - val_loss: 0.2127 - val_mse: 0.2127 Epoch 13/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.3254 - mse: 0.3254 - val_loss: 0.2015 - val_mse: 0.2015 Epoch 14/1000 7/7 [==============================] - 0s 57ms/step - loss: 0.3078 - mse: 0.3078 - val_loss: 0.1893 - val_mse: 0.1893 Epoch 15/1000 7/7 [==============================] - 0s 55ms/step - loss: 0.2886 - mse: 0.2886 - val_loss: 0.1764 - val_mse: 0.1764 Epoch 16/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.2683 - mse: 0.2683 - val_loss: 0.1634 - val_mse: 0.1634 Epoch 17/1000 7/7 [==============================] - 0s 49ms/step - loss: 0.2479 - mse: 0.2479 - val_loss: 0.1512 - val_mse: 0.1512 Epoch 18/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.2285 - mse: 0.2285 - val_loss: 0.1393 - val_mse: 0.1393 Epoch 19/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.2090 - mse: 0.2090 - val_loss: 0.1274 - val_mse: 0.1274 Epoch 20/1000 7/7 [==============================] - 0s 57ms/step - loss: 0.1901 - mse: 0.1901 - val_loss: 0.1162 - val_mse: 0.1162 Epoch 21/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.1720 - mse: 0.1720 - val_loss: 0.1066 - val_mse: 0.1066 Epoch 22/1000 7/7 [==============================] - 0s 57ms/step - loss: 0.1583 - mse: 0.1583 - val_loss: 0.1002 - val_mse: 0.1002 Epoch 23/1000 7/7 [==============================] - 0s 62ms/step - loss: 0.1466 - mse: 0.1466 - val_loss: 0.0932 - val_mse: 0.0932 Epoch 24/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.1359 - mse: 0.1359 - val_loss: 0.0867 - val_mse: 0.0867 Epoch 25/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.1256 - mse: 0.1256 - val_loss: 0.0801 - val_mse: 0.0801 Epoch 26/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.1159 - mse: 0.1159 - val_loss: 0.0740 - val_mse: 0.0740 Epoch 27/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.1064 - mse: 0.1064 - val_loss: 0.0687 - val_mse: 0.0687 Epoch 28/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0980 - mse: 0.0980 - val_loss: 0.0633 - val_mse: 0.0633 Epoch 29/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0895 - mse: 0.0895 - val_loss: 0.0574 - val_mse: 0.0574 Epoch 30/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0814 - mse: 0.0814 - val_loss: 0.0529 - val_mse: 0.0529 Epoch 31/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0757 - mse: 0.0757 - val_loss: 0.0507 - val_mse: 0.0507 Epoch 32/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0716 - mse: 0.0716 - val_loss: 0.0488 - val_mse: 0.0488 Epoch 33/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0684 - mse: 0.0684 - val_loss: 0.0470 - val_mse: 0.0470 Epoch 34/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0652 - mse: 0.0652 - val_loss: 0.0451 - val_mse: 0.0451 Epoch 35/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0623 - mse: 0.0623 - val_loss: 0.0432 - val_mse: 0.0432 Epoch 36/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0595 - mse: 0.0595 - val_loss: 0.0413 - val_mse: 0.0413 Epoch 37/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0569 - mse: 0.0569 - val_loss: 0.0394 - val_mse: 0.0394 Epoch 38/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0545 - mse: 0.0545 - val_loss: 0.0382 - val_mse: 0.0382 Epoch 39/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0523 - mse: 0.0523 - val_loss: 0.0368 - val_mse: 0.0368 Epoch 40/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0501 - mse: 0.0501 - val_loss: 0.0355 - val_mse: 0.0355 Epoch 41/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0482 - mse: 0.0482 - val_loss: 0.0344 - val_mse: 0.0344 Epoch 42/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0464 - mse: 0.0464 - val_loss: 0.0334 - val_mse: 0.0334 Epoch 43/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0446 - mse: 0.0446 - val_loss: 0.0319 - val_mse: 0.0319 Epoch 44/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0429 - mse: 0.0429 - val_loss: 0.0310 - val_mse: 0.0310 Epoch 45/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0413 - mse: 0.0413 - val_loss: 0.0302 - val_mse: 0.0302 Epoch 46/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0398 - mse: 0.0398 - val_loss: 0.0293 - val_mse: 0.0293 Epoch 47/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0383 - mse: 0.0383 - val_loss: 0.0282 - val_mse: 0.0282 Epoch 48/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0369 - mse: 0.0369 - val_loss: 0.0271 - val_mse: 0.0271 Epoch 49/1000 7/7 [==============================] - 0s 50ms/step - loss: 0.0356 - mse: 0.0356 - val_loss: 0.0265 - val_mse: 0.0265 Epoch 50/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0343 - mse: 0.0343 - val_loss: 0.0259 - val_mse: 0.0259 Epoch 51/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0331 - mse: 0.0331 - val_loss: 0.0248 - val_mse: 0.0248 Epoch 52/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0320 - mse: 0.0320 - val_loss: 0.0239 - val_mse: 0.0239 Epoch 53/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0309 - mse: 0.0309 - val_loss: 0.0235 - val_mse: 0.0235 Epoch 54/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0298 - mse: 0.0298 - val_loss: 0.0228 - val_mse: 0.0228 Epoch 55/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0288 - mse: 0.0288 - val_loss: 0.0222 - val_mse: 0.0222 Epoch 56/1000 7/7 [==============================] - 0s 49ms/step - loss: 0.0278 - mse: 0.0278 - val_loss: 0.0216 - val_mse: 0.0216 Epoch 57/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0269 - mse: 0.0269 - val_loss: 0.0212 - val_mse: 0.0212 Epoch 58/1000 7/7 [==============================] - 0s 52ms/step - loss: 0.0260 - mse: 0.0260 - val_loss: 0.0203 - val_mse: 0.0203 Epoch 59/1000 7/7 [==============================] - 0s 48ms/step - loss: 0.0252 - mse: 0.0252 - val_loss: 0.0199 - val_mse: 0.0199 Epoch 60/1000 7/7 [==============================] - 0s 55ms/step - loss: 0.0244 - mse: 0.0244 - val_loss: 0.0195 - val_mse: 0.0195 Epoch 61/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0236 - mse: 0.0236 - val_loss: 0.0189 - val_mse: 0.0189 Epoch 62/1000 7/7 [==============================] - 0s 60ms/step - loss: 0.0229 - mse: 0.0229 - val_loss: 0.0183 - val_mse: 0.0183 Epoch 63/1000 7/7 [==============================] - 0s 61ms/step - loss: 0.0221 - mse: 0.0221 - val_loss: 0.0179 - val_mse: 0.0179 Epoch 64/1000 7/7 [==============================] - 0s 66ms/step - loss: 0.0214 - mse: 0.0214 - val_loss: 0.0177 - val_mse: 0.0177 Epoch 65/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0208 - mse: 0.0208 - val_loss: 0.0171 - val_mse: 0.0171 Epoch 66/1000 7/7 [==============================] - 0s 51ms/step - loss: 0.0201 - mse: 0.0201 - val_loss: 0.0168 - val_mse: 0.0168 Epoch 67/1000 7/7 [==============================] - 0s 53ms/step - loss: 0.0195 - mse: 0.0195 - val_loss: 0.0165 - val_mse: 0.0165 Epoch 68/1000 7/7 [==============================] - 0s 51ms/step - loss: 0.0189 - mse: 0.0189 - val_loss: 0.0159 - val_mse: 0.0159 Epoch 69/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0184 - mse: 0.0184 - val_loss: 0.0158 - val_mse: 0.0158 Epoch 70/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0179 - mse: 0.0179 - val_loss: 0.0152 - val_mse: 0.0152 Epoch 71/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0174 - mse: 0.0174 - val_loss: 0.0148 - val_mse: 0.0148 Epoch 72/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0169 - mse: 0.0169 - val_loss: 0.0151 - val_mse: 0.0151 Epoch 73/1000 7/7 [==============================] - 0s 52ms/step - loss: 0.0164 - mse: 0.0164 - val_loss: 0.0145 - val_mse: 0.0145 Epoch 74/1000 7/7 [==============================] - 0s 61ms/step - loss: 0.0159 - mse: 0.0159 - val_loss: 0.0139 - val_mse: 0.0139 Epoch 75/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0155 - mse: 0.0155 - val_loss: 0.0141 - val_mse: 0.0141 Epoch 76/1000 7/7 [==============================] - 0s 57ms/step - loss: 0.0151 - mse: 0.0151 - val_loss: 0.0136 - val_mse: 0.0136 Epoch 77/1000 7/7 [==============================] - 0s 57ms/step - loss: 0.0139 - mse: 0.0139 - val_loss: 0.0121 - val_mse: 0.0121 Epoch 78/1000 7/7 [==============================] - 0s 65ms/step - loss: 0.0122 - mse: 0.0122 - val_loss: 0.0112 - val_mse: 0.0112 Epoch 79/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0113 - mse: 0.0113 - val_loss: 0.0111 - val_mse: 0.0111 Epoch 80/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0108 - mse: 0.0108 - val_loss: 0.0107 - val_mse: 0.0107 Epoch 81/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0104 - mse: 0.0104 - val_loss: 0.0106 - val_mse: 0.0106 Epoch 82/1000 7/7 [==============================] - 0s 69ms/step - loss: 0.0099 - mse: 0.0099 - val_loss: 0.0102 - val_mse: 0.0102 Epoch 83/1000 7/7 [==============================] - 0s 67ms/step - loss: 0.0096 - mse: 0.0096 - val_loss: 0.0100 - val_mse: 0.0100 Epoch 84/1000 7/7 [==============================] - 0s 58ms/step - loss: 0.0093 - mse: 0.0093 - val_loss: 0.0096 - val_mse: 0.0096 Epoch 85/1000 7/7 [==============================] - 0s 59ms/step - loss: 0.0090 - mse: 0.0090 - val_loss: 0.0100 - val_mse: 0.0100 Epoch 86/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0087 - mse: 0.0087 - val_loss: 0.0095 - val_mse: 0.0095 Epoch 87/1000 7/7 [==============================] - 0s 48ms/step - loss: 0.0085 - mse: 0.0085 - val_loss: 0.0094 - val_mse: 0.0094 Epoch 88/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0082 - mse: 0.0082 - val_loss: 0.0095 - val_mse: 0.0095 Epoch 89/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.0080 - mse: 0.0080 - val_loss: 0.0093 - val_mse: 0.0093 Epoch 90/1000 7/7 [==============================] - 0s 52ms/step - loss: 0.0078 - mse: 0.0078 - val_loss: 0.0093 - val_mse: 0.0093 Epoch 91/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0077 - mse: 0.0077 - val_loss: 0.0090 - val_mse: 0.0090 Epoch 92/1000 7/7 [==============================] - 0s 55ms/step - loss: 0.0076 - mse: 0.0076 - val_loss: 0.0087 - val_mse: 0.0087 Epoch 93/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0074 - mse: 0.0074 - val_loss: 0.0088 - val_mse: 0.0088 Epoch 94/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0073 - mse: 0.0073 - val_loss: 0.0085 - val_mse: 0.0085 Epoch 95/1000 7/7 [==============================] - 0s 58ms/step - loss: 0.0072 - mse: 0.0072 - val_loss: 0.0086 - val_mse: 0.0086 Epoch 96/1000 7/7 [==============================] - 0s 66ms/step - loss: 0.0070 - mse: 0.0070 - val_loss: 0.0089 - val_mse: 0.0089 Epoch 97/1000 7/7 [==============================] - 0s 66ms/step - loss: 0.0069 - mse: 0.0069 - val_loss: 0.0084 - val_mse: 0.0084 Epoch 98/1000 7/7 [==============================] - 0s 62ms/step - loss: 0.0069 - mse: 0.0069 - val_loss: 0.0084 - val_mse: 0.0084 Epoch 99/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0067 - mse: 0.0067 - val_loss: 0.0088 - val_mse: 0.0088 Epoch 100/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0066 - mse: 0.0066 - val_loss: 0.0083 - val_mse: 0.0083 Epoch 101/1000 7/7 [==============================] - 0s 62ms/step - loss: 0.0065 - mse: 0.0065 - val_loss: 0.0082 - val_mse: 0.0082 Epoch 102/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0065 - mse: 0.0065 - val_loss: 0.0085 - val_mse: 0.0085 Epoch 103/1000 7/7 [==============================] - 0s 44ms/step - loss: 0.0064 - mse: 0.0064 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 104/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0063 - mse: 0.0063 - val_loss: 0.0083 - val_mse: 0.0083 Epoch 105/1000 7/7 [==============================] - 0s 52ms/step - loss: 0.0062 - mse: 0.0062 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 106/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0062 - mse: 0.0062 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 107/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0061 - mse: 0.0061 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 108/1000 7/7 [==============================] - 0s 52ms/step - loss: 0.0061 - mse: 0.0061 - val_loss: 0.0082 - val_mse: 0.0082 Epoch 109/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0061 - mse: 0.0061 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 110/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0060 - mse: 0.0060 - val_loss: 0.0083 - val_mse: 0.0083 Epoch 111/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0060 - mse: 0.0060 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 112/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0059 - mse: 0.0059 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 113/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0059 - mse: 0.0059 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 114/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0058 - mse: 0.0058 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 115/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0058 - mse: 0.0058 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 116/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0058 - mse: 0.0058 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 117/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 118/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 119/1000 7/7 [==============================] - 0s 60ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 120/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 121/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 122/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 123/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 124/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 125/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 126/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 127/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 128/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 129/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 130/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0082 - val_mse: 0.0082 Epoch 131/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 132/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 133/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 134/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 135/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 136/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 137/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 138/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 139/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 140/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 141/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 142/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 143/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 144/1000 7/7 [==============================] - 0s 48ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 145/1000 7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 146/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 147/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 148/1000 7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0082 - val_mse: 0.0082 Epoch 149/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 150/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 151/1000 7/7 [==============================] - 0s 60ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 152/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 153/1000 7/7 [==============================] - 0s 66ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 154/1000 7/7 [==============================] - 0s 53ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 155/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 156/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 157/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 158/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 159/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 160/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 161/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 162/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 163/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 164/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 165/1000 7/7 [==============================] - 0s 51ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 166/1000 7/7 [==============================] - 0s 63ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 167/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 168/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 169/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 170/1000 7/7 [==============================] - 0s 53ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 171/1000 7/7 [==============================] - 0s 48ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 172/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 173/1000 7/7 [==============================] - 0s 63ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 174/1000 7/7 [==============================] - 0s 64ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 175/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 176/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 177/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 178/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 179/1000 7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 180/1000 7/7 [==============================] - 0s 59ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 181/1000 7/7 [==============================] - 0s 62ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 182/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 183/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 184/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 185/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 186/1000 7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 187/1000 7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 188/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 189/1000 7/7 [==============================] - 0s 52ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 190/1000 7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 191/1000 7/7 [==============================] - 0s 57ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 192/1000 7/7 [==============================] - 0s 53ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 193/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 194/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 195/1000 7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 196/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 197/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 198/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 199/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 200/1000 7/7 [==============================] - 0s 51ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 201/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 202/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 203/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 204/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 205/1000 7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 206/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 207/1000 7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 208/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 209/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 210/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 211/1000 7/7 [==============================] - 0s 66ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 212/1000 7/7 [==============================] - 0s 48ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 213/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 214/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 215/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 216/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 217/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 218/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 219/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 220/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 221/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 222/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 223/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 224/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 225/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 226/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 227/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 228/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 229/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 230/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 231/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 232/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 233/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 234/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 235/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 236/1000 7/7 [==============================] - 0s 44ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 237/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 238/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 239/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 240/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 241/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 242/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 243/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 244/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 245/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 246/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 247/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 248/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 249/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 250/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 251/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 252/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 253/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 254/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 255/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 256/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 257/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 258/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 259/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 260/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 261/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 262/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 263/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 264/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 265/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 266/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 267/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 268/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 269/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 270/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 271/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 272/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 273/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 274/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 275/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 276/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 277/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 278/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 279/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 280/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 281/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 282/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 283/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 284/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 285/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 286/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 287/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 288/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 289/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 290/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 291/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 292/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 293/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 294/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 295/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 296/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 297/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 298/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 299/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 300/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0082 - val_mse: 0.0082 Epoch 301/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 302/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0083 - val_mse: 0.0083 Epoch 303/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 304/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 305/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 306/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 307/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 308/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 309/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 310/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 311/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 312/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 313/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 314/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 315/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 316/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 317/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 318/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 319/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 320/1000 7/7 [==============================] - 0s 44ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 321/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 322/1000 7/7 [==============================] - 0s 48ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 323/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 324/1000 7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 325/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 326/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 327/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 328/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 329/1000 7/7 [==============================] - 0s 53ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 330/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 331/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 332/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 333/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 334/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 335/1000 7/7 [==============================] - 0s 51ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 336/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 337/1000 7/7 [==============================] - 0s 48ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 338/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 339/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 340/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 341/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 342/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 343/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 344/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 345/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 346/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 347/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 348/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 349/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 350/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 351/1000 7/7 [==============================] - 0s 48ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 352/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 353/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 354/1000 7/7 [==============================] - 0s 61ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 355/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 356/1000 7/7 [==============================] - 0s 55ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 357/1000 7/7 [==============================] - 0s 52ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 358/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 359/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 360/1000 7/7 [==============================] - 0s 52ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 361/1000 7/7 [==============================] - 0s 50ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 362/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 363/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 364/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 365/1000 7/7 [==============================] - 0s 59ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 366/1000 7/7 [==============================] - 0s 53ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 367/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 368/1000 7/7 [==============================] - 0s 61ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 369/1000 7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 370/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 371/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 372/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 373/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 374/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 375/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 376/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 377/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 378/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 379/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 380/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 381/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 382/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 383/1000 7/7 [==============================] - 0s 59ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 384/1000 7/7 [==============================] - 0s 59ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 385/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 386/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 387/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 388/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 389/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 390/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 391/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 392/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 393/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 394/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 395/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 396/1000 7/7 [==============================] - 0s 61ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 397/1000 7/7 [==============================] - 0s 60ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 398/1000 7/7 [==============================] - 0s 57ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 399/1000 7/7 [==============================] - 0s 64ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 400/1000 7/7 [==============================] - 0s 66ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 401/1000 7/7 [==============================] - 0s 65ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 402/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 403/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 404/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 405/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 406/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 407/1000 7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 408/1000 7/7 [==============================] - 0s 65ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 409/1000 7/7 [==============================] - 0s 66ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 410/1000 7/7 [==============================] - 0s 68ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 411/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 412/1000 7/7 [==============================] - 0s 48ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 413/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 414/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 415/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 416/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 417/1000 7/7 [==============================] - 0s 61ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 418/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 419/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 420/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 421/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 422/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 423/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 424/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 425/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 426/1000 7/7 [==============================] - 0s 55ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 427/1000 7/7 [==============================] - 0s 57ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 428/1000 7/7 [==============================] - 0s 65ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 429/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 430/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 431/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 432/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 433/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 434/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 435/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 436/1000 7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 437/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 438/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 439/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 440/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 441/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 442/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 443/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 444/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 445/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 446/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 447/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 448/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 449/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 450/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 451/1000 7/7 [==============================] - 0s 61ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 452/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 453/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 454/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 455/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 456/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 457/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 458/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 459/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 460/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 461/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 462/1000 7/7 [==============================] - 0s 58ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 463/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 464/1000 7/7 [==============================] - 0s 48ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 465/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 466/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 467/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 468/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 469/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 470/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 471/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 472/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 473/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 474/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 475/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 476/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 477/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 478/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 479/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 480/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 481/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 482/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 483/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 484/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 485/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 486/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 487/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 488/1000 7/7 [==============================] - 0s 59ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 489/1000 7/7 [==============================] - 0s 55ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 490/1000 7/7 [==============================] - 0s 51ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 491/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 492/1000 7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 493/1000 7/7 [==============================] - 0s 57ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 494/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 495/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 496/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 497/1000 7/7 [==============================] - 0s 55ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 498/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 499/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 500/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 501/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 502/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 503/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 504/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 505/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 506/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 507/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 508/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 509/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 510/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 511/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 512/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 513/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 514/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 515/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 516/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 517/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 518/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 519/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 520/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 521/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 522/1000 7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 523/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 524/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 525/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 526/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 527/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 528/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 529/1000 7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 530/1000 7/7 [==============================] - 0s 51ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 531/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 532/1000 7/7 [==============================] - 0s 62ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 533/1000 7/7 [==============================] - 0s 51ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 534/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 535/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 536/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 537/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 538/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 539/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 540/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 541/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 542/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 543/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 544/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 545/1000 7/7 [==============================] - 0s 52ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 546/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 547/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 548/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 549/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 550/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 551/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 552/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 553/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 554/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 555/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 556/1000 7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 557/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 558/1000 7/7 [==============================] - 0s 53ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 559/1000 7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 560/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 561/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 562/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 563/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 564/1000 7/7 [==============================] - 0s 59ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 565/1000 7/7 [==============================] - 0s 49ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 566/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 567/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 568/1000 7/7 [==============================] - 0s 44ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 569/1000 7/7 [==============================] - 0s 44ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 570/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 571/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 572/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 573/1000 7/7 [==============================] - 0s 51ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 574/1000 7/7 [==============================] - 0s 60ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 575/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 576/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 577/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 578/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 579/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 580/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 581/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 582/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 583/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 584/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 585/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 586/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 587/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 588/1000 7/7 [==============================] - 0s 44ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 589/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 590/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 591/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 592/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 593/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 594/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 595/1000 7/7 [==============================] - 0s 49ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 596/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 597/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 598/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 599/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 600/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 601/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 602/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 603/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 604/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 605/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 606/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 607/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 608/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 609/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 610/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 611/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 612/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 613/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 614/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 615/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 616/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 617/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 618/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 619/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 620/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 621/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 622/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 623/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 624/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 625/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 626/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0082 - val_mse: 0.0082 Epoch 627/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 628/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 629/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 630/1000 7/7 [==============================] - 0s 20ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 631/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 632/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 633/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 634/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 635/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 636/1000 7/7 [==============================] - 0s 57ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 637/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 638/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 639/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 640/1000 7/7 [==============================] - 0s 50ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 641/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 642/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 643/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 644/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 645/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 646/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 647/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 648/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 649/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 650/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 651/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 652/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 653/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 654/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 655/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 656/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 657/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 658/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 659/1000 7/7 [==============================] - 0s 57ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 660/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 661/1000 7/7 [==============================] - 0s 53ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 662/1000 7/7 [==============================] - 0s 54ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 663/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 664/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 665/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 666/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 667/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 668/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 669/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 670/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 671/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 672/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 673/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 674/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 675/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 676/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 677/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 678/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 679/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 680/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 681/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 682/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 683/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 684/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 685/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 686/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 687/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 688/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 689/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 690/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 691/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 692/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 693/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 694/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 695/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 696/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 697/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 698/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 699/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 700/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 701/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 702/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 703/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 704/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 705/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 706/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 707/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 708/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 709/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 710/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 711/1000 7/7 [==============================] - 0s 24ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 712/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 713/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 714/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 715/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 716/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 717/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 718/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 719/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 720/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 721/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 722/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 723/1000 7/7 [==============================] - 0s 18ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 724/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 725/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 726/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 727/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 728/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 729/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 730/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 731/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 732/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 733/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 734/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 735/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 736/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 737/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 738/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 739/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 740/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 741/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 742/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 743/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 744/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 745/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 746/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 747/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 748/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 749/1000 7/7 [==============================] - 0s 19ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 750/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 751/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 752/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 753/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 754/1000 7/7 [==============================] - 0s 21ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 755/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 756/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 757/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 758/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 759/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 760/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 761/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 762/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 763/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 764/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 765/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 766/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 767/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 768/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 769/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 770/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 771/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 772/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 773/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 774/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 775/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 776/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 777/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 778/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 779/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 780/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 781/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 782/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 783/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 784/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 785/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 786/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 787/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 788/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 789/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 790/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 791/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 792/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 793/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 794/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 795/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 796/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 797/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 798/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 799/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 800/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 801/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 802/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 803/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 804/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 805/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 806/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 807/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 808/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 809/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 810/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 811/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 812/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 813/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 814/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 815/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 816/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 817/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 818/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 819/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 820/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 821/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 822/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 823/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 824/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 825/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 826/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 827/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 828/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 829/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 830/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 831/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 832/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 833/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 834/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 835/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 836/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 837/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 838/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 839/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 840/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 841/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 842/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 843/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 844/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 845/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 846/1000 7/7 [==============================] - 0s 48ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 847/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 848/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0072 - val_mse: 0.0072 Epoch 849/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 850/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0056 - mse: 0.0056 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 851/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0072 - val_mse: 0.0072 Epoch 852/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 853/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 854/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 855/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 856/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 857/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 858/1000 7/7 [==============================] - 0s 40ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 859/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 860/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 861/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 862/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 863/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 864/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 865/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 866/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 867/1000 7/7 [==============================] - 0s 44ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 868/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 869/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 870/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 871/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 872/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 873/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 874/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 875/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 876/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 877/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 878/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 879/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 880/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 881/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 882/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 883/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 884/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 885/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 886/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 887/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 888/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 889/1000 7/7 [==============================] - 0s 51ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 890/1000 7/7 [==============================] - 0s 48ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 891/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 892/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 893/1000 7/7 [==============================] - 0s 50ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 894/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 895/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 896/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 897/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 898/1000 7/7 [==============================] - 0s 53ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 899/1000 7/7 [==============================] - 0s 53ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 900/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 901/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 902/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 903/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 904/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 905/1000 7/7 [==============================] - 0s 50ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 906/1000 7/7 [==============================] - 0s 59ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 907/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 908/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 909/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 910/1000 7/7 [==============================] - 0s 39ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 911/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 912/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 913/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 914/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 915/1000 7/7 [==============================] - 0s 53ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 916/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 917/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 918/1000 7/7 [==============================] - 0s 52ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 919/1000 7/7 [==============================] - 0s 52ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 920/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 921/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 922/1000 7/7 [==============================] - 0s 58ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 923/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 924/1000 7/7 [==============================] - 0s 56ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 925/1000 7/7 [==============================] - 0s 55ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 926/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 927/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 928/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 929/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 930/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 931/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 932/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 933/1000 7/7 [==============================] - 0s 51ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 934/1000 7/7 [==============================] - 0s 45ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 935/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 936/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 937/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 938/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 939/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 940/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 941/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 942/1000 7/7 [==============================] - 0s 36ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 943/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 944/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 945/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 946/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 947/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 948/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 949/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 950/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 951/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 952/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 953/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 954/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 955/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 956/1000 7/7 [==============================] - 0s 35ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 957/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 958/1000 7/7 [==============================] - 0s 30ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 959/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 960/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 961/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 962/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 963/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 964/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 965/1000 7/7 [==============================] - 0s 37ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 966/1000 7/7 [==============================] - 0s 32ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 967/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 968/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 969/1000 7/7 [==============================] - 0s 23ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 970/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 971/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 972/1000 7/7 [==============================] - 0s 33ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 973/1000 7/7 [==============================] - 0s 38ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0079 - val_mse: 0.0079 Epoch 974/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 975/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 976/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 977/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 978/1000 7/7 [==============================] - 0s 46ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 979/1000 7/7 [==============================] - 0s 47ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 980/1000 7/7 [==============================] - 0s 42ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 981/1000 7/7 [==============================] - 0s 29ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0075 - val_mse: 0.0075 Epoch 982/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 983/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 984/1000 7/7 [==============================] - 0s 52ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 985/1000 7/7 [==============================] - 0s 41ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 986/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 987/1000 7/7 [==============================] - 0s 28ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 988/1000 7/7 [==============================] - 0s 43ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0077 - val_mse: 0.0077 Epoch 989/1000 7/7 [==============================] - 0s 34ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 990/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 991/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0078 - val_mse: 0.0078 Epoch 992/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0072 - val_mse: 0.0072 Epoch 993/1000 7/7 [==============================] - 0s 25ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0081 - val_mse: 0.0081 Epoch 994/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0072 - val_mse: 0.0072 Epoch 995/1000 7/7 [==============================] - 0s 26ms/step - loss: 0.0055 - mse: 0.0055 - val_loss: 0.0074 - val_mse: 0.0074 Epoch 996/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0080 - val_mse: 0.0080 Epoch 997/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0073 - val_mse: 0.0073 Epoch 998/1000 7/7 [==============================] - 0s 31ms/step - loss: 0.0054 - mse: 0.0054 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 999/1000 7/7 [==============================] - 0s 27ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076 Epoch 1000/1000 7/7 [==============================] - 0s 22ms/step - loss: 0.0053 - mse: 0.0053 - val_loss: 0.0076 - val_mse: 0.0076
plt.subplots(figsize=(14,8))
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
sns.despine()
plt.show()
trainin_predi = (lstm_model.predict(X_train10))
cv_predi = (lstm_model.predict(X_cv10))
test_predi = (lstm_model.predict(X_test10))
7/7 [==============================] - 3s 6ms/step 3/3 [==============================] - 0s 11ms/step 3/3 [==============================] - 0s 7ms/step
plt.subplots(figsize=(14,8))
plt.plot(y_train10.flatten()*m)
plt.plot(trainin_predi.flatten()*m)
sns.despine()
plt.show()
plt.subplots(figsize=(14,8))
plt.plot(y_cv10.flatten()*m)
plt.plot(cv_predi.flatten()*m)
sns.despine()
plt.show()
plt.subplots(figsize=(14,8))
plt.plot(y_test10.flatten()*m)
plt.plot(test_predi.flatten()*m)
sns.despine()
plt.show()
from scipy.stats import t
sample_mean = np.mean((lstm_model.predict(y_test[-1].reshape(1,7,1))*m).flatten())
sample_std = np.std((lstm_model.predict(y_test[-1].reshape(1,7,1))*m).flatten(), ddof=1) # ddof=1 for sample standard deviation
# Desired confidence level (e.g., 95%)
confidence_level = 0.95
# Degrees of freedom for a time series is typically sample size - 1
degrees_of_freedom = 7 - 1
# Calculate the t-score
t_score = t.ppf((1 + confidence_level) / 2, degrees_of_freedom)
# Calculate the margin of error
margin_of_error = t_score * (sample_std / np.sqrt(7))
# Confidence interval
lower_bound = sample_mean - margin_of_error
upper_bound = sample_mean + margin_of_error
1/1 [==============================] - 0s 40ms/step 1/1 [==============================] - 0s 30ms/step
plt.subplots(figsize=(14,8))
plt.plot(time_series[-7:].values)
plt.plot(np.append(time_series[-7:].values,(lstm_model.predict(y_test[-1].reshape(1,7,1))*m).flatten()),'--')
plt.plot(np.append(time_series[-7:].values,np.array([lower_bound]*7)),'--')
plt.plot(np.append(time_series[-7:].values,np.array([upper_bound]*7)),'--')
sns.despine()
plt.show()
1/1 [==============================] - 0s 24ms/step